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    "import numpy as np\n",
    "import pandas as pd\n",
    "import xarray as xr\n",
    "import zarr\n",
    "import gcsfs\n",
    "\n",
    "from matplotlib import pyplot as plt"
   ]
  },
  {
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   "execution_count": 7,
   "id": "69cf94f8-581b-4413-9777-be7b0ff47520",
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    "gcs = gcsfs.GCSFileSystem()\n",
    "\n",
    "def read_dcenti_google_cloud(en):\n",
    "    \"\"\"\n",
    "    if en is 0:  read ensemble mean\n",
    "    if en is 1-200: read individual members\n",
    "    if en is -1: read climatology\n",
    "    \"\"\"\n",
    "    remote = 'gs://dcent-i-zarr/'\n",
    "    \n",
    "    if en == 0:\n",
    "        store_value  = remote + 'DCENT_I_1.1.0.0_mean_spread_ts.zarr/'\n",
    "    elif en == -1:\n",
    "        store_value  = remote + 'DCENT_I_1.1.0.0_mean_spread_lsat.zarr/'\n",
    "    elif en == -2:\n",
    "        store_value  = remote + 'DCENT_I_1.1.0.0_mean_spread_sst.zarr/'\n",
    "    elif en == -3:\n",
    "        store_value  = remote + 'DCENT_I_1.1.0.0_diagnostics.zarr/'\n",
    "    elif en == -4:\n",
    "        store_value  = remote + 'DCENT_I_1.1.0.0_clim_1982_2014.zarr/'\n",
    "    else:\n",
    "        store_value0 = remote + 'DCENT_I_1.1.0.0_ensemble_zarr/DCENT_I_1.1.0.0_member_{:03d}.zarr/'\n",
    "        store_value  = store_value0.format(en)\n",
    "            \n",
    "    mapper = gcs.get_mapper(store_value)\n",
    "    ds = xr.open_zarr(mapper, consolidated=True)\n",
    "\n",
    "    return ds\n",
    "\n",
    "# ------------------------------\n",
    "def calculate_global_mean_temperature(temperature_data):\n",
    "    \n",
    "    latitudes = np.arange(-87.5, 90, 5)\n",
    "    weights = np.cos(np.radians(latitudes))\n",
    "    weights_2d = np.tile(weights[:, np.newaxis], (1, 72))\n",
    "    masked_temperature_data = np.where(np.isnan(temperature_data), 0, temperature_data)\n",
    "    masked_weights_2d = np.where(np.isnan(temperature_data), 0, weights_2d)\n",
    "    weighted_sum = np.sum(masked_temperature_data * masked_weights_2d)\n",
    "    total_weight = np.sum(masked_weights_2d)\n",
    "    global_mean_temperature = weighted_sum / total_weight\n",
    "    \n",
    "    return global_mean_temperature"
   ]
  },
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   "execution_count": 8,
   "id": "78ca2a65-3011-4fa6-b0c8-c2eafd9c81d1",
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 87MB\n",
       "Dimensions:           (lat: 36, bnds: 2, lon: 72, time: 2100, realization: 1)\n",
       "Coordinates:\n",
       "  * lat               (lat) float32 144B -87.5 -82.5 -77.5 ... 77.5 82.5 87.5\n",
       "  * lon               (lon) float32 288B 2.5 7.5 12.5 17.5 ... 347.5 352.5 357.5\n",
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       "  * time              (time) datetime64[ns] 17kB 1850-01-15T12:00:00 ... 2024...\n",
       "Dimensions without coordinates: bnds\n",
       "Data variables:\n",
       "    lat_bnds          (lat, bnds) float32 288B dask.array&lt;chunksize=(36, 2), meta=np.ndarray&gt;\n",
       "    lon_bnds          (lon, bnds) float32 576B dask.array&lt;chunksize=(72, 2), meta=np.ndarray&gt;\n",
       "    lsat_mean         (time, lat, lon) float32 22MB dask.array&lt;chunksize=(2100, 36, 72), meta=np.ndarray&gt;\n",
       "    lsat_p05          (time, lat, lon) float32 22MB dask.array&lt;chunksize=(2100, 36, 72), meta=np.ndarray&gt;\n",
       "    lsat_p95          (time, lat, lon) float32 22MB dask.array&lt;chunksize=(2100, 36, 72), meta=np.ndarray&gt;\n",
       "    lsat_std          (time, lat, lon) float32 22MB dask.array&lt;chunksize=(2100, 36, 72), meta=np.ndarray&gt;\n",
       "    realization_bnds  (bnds) int32 8B dask.array&lt;chunksize=(2,), meta=np.ndarray&gt;\n",
       "    time_bnds         (time, bnds) datetime64[ns] 34kB dask.array&lt;chunksize=(2100, 2), meta=np.ndarray&gt;\n",
       "Attributes:\n",
       "    Conventions:  CF-1.7\n",
       "    comment:      Temperature is expressed as monthly anomalies relative to 1...\n",
       "    history:      Built on 2025-10-08 at 14:49:50\n",
       "    institution:  University of Southampton / National Oceanography Centre\n",
       "    name:         DCENT_I\n",
       "    source:       DCENT Version 2.0\n",
       "    title:        An infilled version of DCENT: Dynamically Consistent ENsemb...\n",
       "    version:      1.1.0.0</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-1f3e919f-4e04-413a-a6e9-ff9480d2dd25' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-1f3e919f-4e04-413a-a6e9-ff9480d2dd25' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>lat</span>: 36</li><li><span>bnds</span>: 2</li><li><span class='xr-has-index'>lon</span>: 72</li><li><span class='xr-has-index'>time</span>: 2100</li><li><span class='xr-has-index'>realization</span>: 1</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-c92dd3c8-42a0-4754-8a6d-3ca4b11df1f9' class='xr-section-summary-in' type='checkbox'  checked><label for='section-c92dd3c8-42a0-4754-8a6d-3ca4b11df1f9' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-87.5 -82.5 -77.5 ... 82.5 87.5</div><input id='attrs-ccb5cb35-e7a0-4755-b23b-03d3b3d51f4a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ccb5cb35-e7a0-4755-b23b-03d3b3d51f4a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b4b078d3-0e10-4cf7-be3c-c971853ee490' class='xr-var-data-in' type='checkbox'><label for='data-b4b078d3-0e10-4cf7-be3c-c971853ee490' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Y</dd><dt><span>bounds :</span></dt><dd>lat_bnds</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>array([-87.5, -82.5, -77.5, -72.5, -67.5, -62.5, -57.5, -52.5, -47.5, -42.5,\n",
       "       -37.5, -32.5, -27.5, -22.5, -17.5, -12.5,  -7.5,  -2.5,   2.5,   7.5,\n",
       "        12.5,  17.5,  22.5,  27.5,  32.5,  37.5,  42.5,  47.5,  52.5,  57.5,\n",
       "        62.5,  67.5,  72.5,  77.5,  82.5,  87.5], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>2.5 7.5 12.5 ... 347.5 352.5 357.5</div><input id='attrs-90559b78-4a43-49ba-a920-b4275a61766c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-90559b78-4a43-49ba-a920-b4275a61766c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-77b47db8-7902-4ab2-9ebe-eb28f32a2cd7' class='xr-var-data-in' type='checkbox'><label for='data-77b47db8-7902-4ab2-9ebe-eb28f32a2cd7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>X</dd><dt><span>bounds :</span></dt><dd>lon_bnds</dd><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>array([  2.5,   7.5,  12.5,  17.5,  22.5,  27.5,  32.5,  37.5,  42.5,  47.5,\n",
       "        52.5,  57.5,  62.5,  67.5,  72.5,  77.5,  82.5,  87.5,  92.5,  97.5,\n",
       "       102.5, 107.5, 112.5, 117.5, 122.5, 127.5, 132.5, 137.5, 142.5, 147.5,\n",
       "       152.5, 157.5, 162.5, 167.5, 172.5, 177.5, 182.5, 187.5, 192.5, 197.5,\n",
       "       202.5, 207.5, 212.5, 217.5, 222.5, 227.5, 232.5, 237.5, 242.5, 247.5,\n",
       "       252.5, 257.5, 262.5, 267.5, 272.5, 277.5, 282.5, 287.5, 292.5, 297.5,\n",
       "       302.5, 307.5, 312.5, 317.5, 322.5, 327.5, 332.5, 337.5, 342.5, 347.5,\n",
       "       352.5, 357.5], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>realization</span></div><div class='xr-var-dims'>(realization)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>-2147483647</div><input id='attrs-53caedf6-34cc-4d87-b03d-47fd963d457a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-53caedf6-34cc-4d87-b03d-47fd963d457a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-438ac8e3-a859-40bf-b202-7b179f59cc36' class='xr-var-data-in' type='checkbox'><label for='data-438ac8e3-a859-40bf-b202-7b179f59cc36' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>realization_bound :</span></dt><dd>realization_bnds</dd><dt><span>standard_name :</span></dt><dd>realization</dd><dt><span>units :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><pre>array([-2147483647], dtype=int32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1850-01-15T12:00:00 ... 2024-12-...</div><input id='attrs-8f274464-4259-4694-b324-bf5777e9318f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8f274464-4259-4694-b324-bf5777e9318f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6a0cb915-15e1-49cc-af50-5290f6802a2d' class='xr-var-data-in' type='checkbox'><label for='data-6a0cb915-15e1-49cc-af50-5290f6802a2d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>T</dd><dt><span>bounds :</span></dt><dd>time_bnds</dd><dt><span>long_name :</span></dt><dd>time</dd><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;1850-01-15T12:00:00.000000000&#x27;, &#x27;1850-02-15T12:00:00.000000000&#x27;,\n",
       "       &#x27;1850-03-15T12:00:00.000000000&#x27;, ..., &#x27;2024-10-15T12:00:00.000000000&#x27;,\n",
       "       &#x27;2024-11-15T12:00:00.000000000&#x27;, &#x27;2024-12-15T12:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-435d3986-ed1a-4b59-97f6-9c366ad62fab' class='xr-section-summary-in' type='checkbox'  checked><label for='section-435d3986-ed1a-4b59-97f6-9c366ad62fab' class='xr-section-summary' >Data variables: <span>(8)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>lat_bnds</span></div><div class='xr-var-dims'>(lat, bnds)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(36, 2), meta=np.ndarray&gt;</div><input id='attrs-ad6994ed-a66e-4f07-bb28-10f5370e22f7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ad6994ed-a66e-4f07-bb28-10f5370e22f7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f75f12a6-5c7c-4aa2-af6f-9089e207c2ae' class='xr-var-data-in' type='checkbox'><label for='data-f75f12a6-5c7c-4aa2-af6f-9089e207c2ae' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>latitude_bounds</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                        <td> 288 B </td>\n",
       "                        <td> 288 B </td>\n",
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       "                        <td> (36, 2) </td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon_bnds</span></div><div class='xr-var-dims'>(lon, bnds)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(72, 2), meta=np.ndarray&gt;</div><input id='attrs-85fa7033-b64b-4145-90e3-b28efdff5ea2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-85fa7033-b64b-4145-90e3-b28efdff5ea2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5caccb4d-d3cc-4328-9ee2-cc1c417d6c5e' class='xr-var-data-in' type='checkbox'><label for='data-5caccb4d-d3cc-4328-9ee2-cc1c417d6c5e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>longitude_bounds</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                        <td> 576 B </td>\n",
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       "                        <td> 20.76 MiB </td>\n",
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       "                        <td> (2100, 36, 72) </td>\n",
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       "                        <td> 32.81 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (2100, 2) </td>\n",
       "                        <td> (2100, 2) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> datetime64[ns] numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
       "        <svg width=\"75\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"25\" y2=\"0\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"0\" y1=\"120\" x2=\"25\" y2=\"120\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"25\" y1=\"0\" x2=\"25\" y2=\"120\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"0.0,0.0 25.412616514582485,0.0 25.412616514582485,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Text -->\n",
       "  <text x=\"12.706308\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2</text>\n",
       "  <text x=\"45.412617\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,45.412617,60.000000)\">2100</text>\n",
       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-704609ed-ab21-41b6-b220-c5e9ac5ec946' class='xr-section-summary-in' type='checkbox'  ><label for='section-704609ed-ab21-41b6-b220-c5e9ac5ec946' class='xr-section-summary' >Indexes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>lat</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-767f008d-4352-41d4-a4a6-833a01a3ef93' class='xr-index-data-in' type='checkbox'/><label for='index-767f008d-4352-41d4-a4a6-833a01a3ef93' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([-87.5, -82.5, -77.5, -72.5, -67.5, -62.5, -57.5, -52.5, -47.5, -42.5,\n",
       "       -37.5, -32.5, -27.5, -22.5, -17.5, -12.5,  -7.5,  -2.5,   2.5,   7.5,\n",
       "        12.5,  17.5,  22.5,  27.5,  32.5,  37.5,  42.5,  47.5,  52.5,  57.5,\n",
       "        62.5,  67.5,  72.5,  77.5,  82.5,  87.5],\n",
       "      dtype=&#x27;float32&#x27;, name=&#x27;lat&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lon</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-5db34ca6-a5c1-47cc-a158-6ffdc168122a' class='xr-index-data-in' type='checkbox'/><label for='index-5db34ca6-a5c1-47cc-a158-6ffdc168122a' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([  2.5,   7.5,  12.5,  17.5,  22.5,  27.5,  32.5,  37.5,  42.5,  47.5,\n",
       "        52.5,  57.5,  62.5,  67.5,  72.5,  77.5,  82.5,  87.5,  92.5,  97.5,\n",
       "       102.5, 107.5, 112.5, 117.5, 122.5, 127.5, 132.5, 137.5, 142.5, 147.5,\n",
       "       152.5, 157.5, 162.5, 167.5, 172.5, 177.5, 182.5, 187.5, 192.5, 197.5,\n",
       "       202.5, 207.5, 212.5, 217.5, 222.5, 227.5, 232.5, 237.5, 242.5, 247.5,\n",
       "       252.5, 257.5, 262.5, 267.5, 272.5, 277.5, 282.5, 287.5, 292.5, 297.5,\n",
       "       302.5, 307.5, 312.5, 317.5, 322.5, 327.5, 332.5, 337.5, 342.5, 347.5,\n",
       "       352.5, 357.5],\n",
       "      dtype=&#x27;float32&#x27;, name=&#x27;lon&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>realization</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-bf1884ea-3862-4e28-a025-e7139b5bff96' class='xr-index-data-in' type='checkbox'/><label for='index-bf1884ea-3862-4e28-a025-e7139b5bff96' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([-2147483647], dtype=&#x27;int32&#x27;, name=&#x27;realization&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-23aafd72-7e65-4c8c-8e89-0ed022befccb' class='xr-index-data-in' type='checkbox'/><label for='index-23aafd72-7e65-4c8c-8e89-0ed022befccb' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;1850-01-15 12:00:00&#x27;, &#x27;1850-02-15 12:00:00&#x27;,\n",
       "               &#x27;1850-03-15 12:00:00&#x27;, &#x27;1850-04-15 12:00:00&#x27;,\n",
       "               &#x27;1850-05-15 12:00:00&#x27;, &#x27;1850-06-15 12:00:00&#x27;,\n",
       "               &#x27;1850-07-15 12:00:00&#x27;, &#x27;1850-08-15 12:00:00&#x27;,\n",
       "               &#x27;1850-09-15 12:00:00&#x27;, &#x27;1850-10-15 12:00:00&#x27;,\n",
       "               ...\n",
       "               &#x27;2024-03-15 12:00:00&#x27;, &#x27;2024-04-15 12:00:00&#x27;,\n",
       "               &#x27;2024-05-15 12:00:00&#x27;, &#x27;2024-06-15 12:00:00&#x27;,\n",
       "               &#x27;2024-07-15 12:00:00&#x27;, &#x27;2024-08-15 12:00:00&#x27;,\n",
       "               &#x27;2024-09-15 12:00:00&#x27;, &#x27;2024-10-15 12:00:00&#x27;,\n",
       "               &#x27;2024-11-15 12:00:00&#x27;, &#x27;2024-12-15 12:00:00&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=2100, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-663886eb-c759-4835-8d86-335fa3e1984e' class='xr-section-summary-in' type='checkbox'  checked><label for='section-663886eb-c759-4835-8d86-335fa3e1984e' class='xr-section-summary' >Attributes: <span>(8)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>Conventions :</span></dt><dd>CF-1.7</dd><dt><span>comment :</span></dt><dd>Temperature is expressed as monthly anomalies relative to 1982-2014 climatology.\n",
       "Values represent air temperature anomalies over land and Sea Surface Temperatsure over the Ocean\n",
       "- tas / blended near surface air temperature anomaly, blended: blended land-sea temperature anomaly\n",
       "- sst / sea surface temperature anomaly: sea surface temperature anomaly\n",
       "- lsat / land surface air temperature anomaly: land and sea ice 2m air temperature anomaly\n",
       "Note: standard names for tas and lsat are the same; there is no land-only CF1.7 standard name.\n",
       "Created using GloMarGridding v1.0.0-rc.2: https://github.com/NOCSurfaceProcesses/GloMarGridding</dd><dt><span>history :</span></dt><dd>Built on 2025-10-08 at 14:49:50</dd><dt><span>institution :</span></dt><dd>University of Southampton / National Oceanography Centre</dd><dt><span>name :</span></dt><dd>DCENT_I</dd><dt><span>source :</span></dt><dd>DCENT Version 2.0</dd><dt><span>title :</span></dt><dd>An infilled version of DCENT: Dynamically Consistent ENsemble of Temperature at the earth surface</dd><dt><span>version :</span></dt><dd>1.1.0.0</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset> Size: 87MB\n",
       "Dimensions:           (lat: 36, bnds: 2, lon: 72, time: 2100, realization: 1)\n",
       "Coordinates:\n",
       "  * lat               (lat) float32 144B -87.5 -82.5 -77.5 ... 77.5 82.5 87.5\n",
       "  * lon               (lon) float32 288B 2.5 7.5 12.5 17.5 ... 347.5 352.5 357.5\n",
       "  * realization       (realization) int32 4B -2147483647\n",
       "  * time              (time) datetime64[ns] 17kB 1850-01-15T12:00:00 ... 2024...\n",
       "Dimensions without coordinates: bnds\n",
       "Data variables:\n",
       "    lat_bnds          (lat, bnds) float32 288B dask.array<chunksize=(36, 2), meta=np.ndarray>\n",
       "    lon_bnds          (lon, bnds) float32 576B dask.array<chunksize=(72, 2), meta=np.ndarray>\n",
       "    lsat_mean         (time, lat, lon) float32 22MB dask.array<chunksize=(2100, 36, 72), meta=np.ndarray>\n",
       "    lsat_p05          (time, lat, lon) float32 22MB dask.array<chunksize=(2100, 36, 72), meta=np.ndarray>\n",
       "    lsat_p95          (time, lat, lon) float32 22MB dask.array<chunksize=(2100, 36, 72), meta=np.ndarray>\n",
       "    lsat_std          (time, lat, lon) float32 22MB dask.array<chunksize=(2100, 36, 72), meta=np.ndarray>\n",
       "    realization_bnds  (bnds) int32 8B dask.array<chunksize=(2,), meta=np.ndarray>\n",
       "    time_bnds         (time, bnds) datetime64[ns] 34kB dask.array<chunksize=(2100, 2), meta=np.ndarray>\n",
       "Attributes:\n",
       "    Conventions:  CF-1.7\n",
       "    comment:      Temperature is expressed as monthly anomalies relative to 1...\n",
       "    history:      Built on 2025-10-08 at 14:49:50\n",
       "    institution:  University of Southampton / National Oceanography Centre\n",
       "    name:         DCENT_I\n",
       "    source:       DCENT Version 2.0\n",
       "    title:        An infilled version of DCENT: Dynamically Consistent ENsemb...\n",
       "    version:      1.1.0.0"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds = read_dcenti_google_cloud(-1)\n",
    "ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "b998bf1d-81ed-465b-b8bf-6e3d004d5146",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ------------------------ Let's start from the ensemble mean ------------------------\n",
    "yr_end = 2024\n",
    "ts     = read_dcenti_google_cloud(0).ts_mean.values         # Read ts\n",
    "lsat   = read_dcenti_google_cloud(-1).lsat_mean.values      # Read lsat\n",
    "sst    = read_dcenti_google_cloud(-2).sst_mean.values       # Read sst\n",
    "\n",
    "GL = np.zeros((temperature.shape[0],3))\n",
    "for ct in np.arange(temperature.shape[0]):\n",
    "    GL[ct,0] = calculate_global_mean_temperature(ts[ct,:,:])     # Calculate global mean while weighting grids by cosine of latitude\n",
    "    GL[ct,1] = calculate_global_mean_temperature(lsat[ct,:,:])   # Calculate global mean while weighting grids by cosine of latitude\n",
    "    GL[ct,2] = calculate_global_mean_temperature(sst[ct,:,:])    # Calculate global mean while weighting grids by cosine of latitude\n",
    "\n",
    "GL_ann = GL.reshape(int(temperature.shape[0]/12),12,3).mean(axis = 1)      # Calculate annual mean of monthly mean\n",
    "plt.plot(np.arange(1850,yr_end+1),GL_ann[:,0],color='k', label = 'TS');  \n",
    "plt.plot(np.arange(1850,yr_end+1),GL_ann[:,1],color='r', label = 'LSAT');  \n",
    "plt.plot(np.arange(1850,yr_end+1),GL_ann[:,2],color='b', label = 'SST');  \n",
    "plt.grid('on')\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "2b813d73-639c-47ce-8d71-edd3edf8500c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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FIHEE7EB0vX13URGEhdVuWafTycyZM7npppt48803GTBgAKNGjWLChAn06dOHYcOG8cgjjzBx4kRuvfVWhgwZwumnn87VV19NXFxcve2zQqFQKJoItRUva9ea733Fi4x3adECHA7Wr18PQL9+/eplF48WFbDbhLjkkktIT0/n+++/5+yzz2bhwoUMGDCAmTNnAvDMM89w8OBB3nzzTXr27Mmbb75JSkoKGzduPLE7rlAoFIrjz9FaXmSQL1TJNJLipW/fvvWzj0dJsxcvoaHCAlKbfwUFHvbvz6OgwFPrdWr6Fxpa9/0NDg7mzDPP5LHHHmPJkiVce+21PPHEE8b8li1bctlll/Hiiy+ydetWEhISePHFF+vxjCkUCoWiSVCbgF1N8xYvbrf3ej41XhqLeGn2biObrfauG49HBGWHhdVPzEt90KNHD2bNmuV3XmBgIJ07d6a4uPj47pRCoVAoTjxWEZKdDWVlEBzsvUx6OmRliWDOoCARS3HokHATgVeadH5+Pnv37gWUeFHUksOHD3PZZZdx/fXX06dPHyIiIli1ahUvvPACF154IT/88AOfffYZEyZMoGvXrmiaxuzZs5kzZw4z/BUdUigUCsXJjTXbCIT1pXNn72nS6pKSIjKLdu4U4qVbNzHd4jbaoKdTt23blpiYGFwuV8Pt+xFQ4qWJEB4eztChQ3nllVfYvXs3LpeLdu3acdNNN/HII4+QkZFBaGgo9913H2lpaQQFBdGlSxemT5/OpEmTTvTuKxQKheJ4Y7W8gIh7qU689OsH+/aZ4kVicRs1FpcRKPHSZAgKCuK5557jueee8zu/U6dOvP3227Xe3r59++ppzxQKhULRKPEVL/7iXmSmUf/+Zt0Qq3ixuI0ak3hpJJEbCoVCoVAo6hUpXmRdMH8ZR1bLS+vW4r0/y0tMTKNJkwYlXhQKhUKhODmR4iUlRbz6ipeCAti9W7yvTrzolhdPixZs2rQJUJYXhUKhUCgUDYUM2O3VS7z6ipdt28RrQoJIhZbiJTPTXEYXL/tLSyktLSU0NJTOvnEzJwAlXhQKhUKhOBmRlpfqxIuMgWnfXrzW4Dbapk/r3bs3DoejAXa2bijxolAoFArFyYamVRUvvgG76eniNSFBvNbgNlqXlgY0DpcRNFPxollLHyuOGnUeFQqFopFSVCQqqwL07CleMzLAWpvlSOKltFQUtgNW7NoFKPFyQggICACgpLZtpBU1Is+jPK8KhUKhaCRIq0tAAHToAE6nsMYcPGguU514yc0VBetkmrTTyQa9sm6PHj0aft9rQbOq8+JwOIiOjuaQripDQ0Pr1NLb4/FQUVFBWVkZ9sbSH+A44/F4KC8v5/Dhw2RnZxMdHd0o/J8KhUKhsCCDdaOjRT+bxET46y8R99KunZjnK15iYsSyHo9oJ2Cp8ZKtv4+Lizt+x1ADzUq8AMTHxwMYAqYuaJpGaWkpISEhdRI9JxPWc9CiRQvjfCoUCoWiESEtL9HR4rVtWyFerHEvvuLFbodWrUS20aFDwgIDaDEx5G7dCkBsbGzD73staHbixWaz0aZNG1q3bl3nvgwul4vff/+d0047rdm6SuQ5GDt2LMG+Db4UCoVC0TjwFS+JieLVmnHkK15AuI6keCkoAMAVGQmI8bOFbNh4gml24kXicDjq7O5wOBy43W6Cg4ObrXiR50C5ihQKhaIR48/yAqZ4KSszq+f6ihfwEi/loaEAtGjRAqezcciGxrEXCoVCoVAo6g8pXqKixKsUL3rKMxkZ4jU42BQ44C1e/vwTgBw9RqaxuIygmWUbKRQKhULRLPC1vHTsKF71lGcvl5E1hlOKlz174KefxCp6enTLli0BKCxsoH2uA0q8KBQKhUJxsmHNNgLo3l28btsmson8xbuAKV4+/VS4lpKT2R0WBgjLS1oatGgBo0aB292wh1ATSrwoFAqFQnGy4Wt56dxZ1HwpKYHU1COLFxkPc+mlHNbfx8bGMn8+VFaKMjAnMvxFiReFQqFQKE42fMWL0wnduon3W7YcWbxILr2U7OxsQIiXefPE5LPOqvc9rhNKvCgUCoVCcbLhK17AdB1t3Vo78ZKUBAMGGOIlJqYV8+eLWePG1fcO1w0lXhQKhUKhONnwzTYCkKX9a2t5ufRSsNkM8VJc3I2cHIiMhCFDGma3a4tKlVYoFAqF4mTDN2AXvMWLFDc1iZfLLgMwxMvevV0AGDtWeKHqWOe1XlHiRaFQKBSKkw1/biMpXrZuFU0aoap4CQ+Hxx8XBeoGDwZM8bJpk1j2RMe7gBIvCoVCoVCcXGiaf/HSpYvoXyStMlBVvAA8+aTXRyFeIti8WbQJONHxLqBiXhQKhUKhOLkoLTV9OlbxEhQEycnm5/BwiIiocVMul4uCggJgNG63jeRks97diUSJF4VCoVAoTiak1cXhAL3AnIF0HYF/q4sPhw8f1t8Jc0tjcBmBEi8KhUKhUJxcWDKNvp89mxkzZpjzZLo01Eq8yHgXh2MUAGecUV87eWyomBeFQqFQKE4m9JgWLTqaCRMmUFpayimnnELXrl3rbHmR4gXaAKJQb2NAWV4UCoVCoTiZ0C0v5cHBlJaWArB48WIxrxrx8tdffzF37lzcPg2LhHixUVkZDUCrVg2103VDiReFQqFQKE4mdPFSaGk+tGTJEvEmJcXsIp2QwKeffsqoUaNISkrinHPO4b333vPalBAvLQAHAHpj6ROOEi8KhUKhUJxM6OIl1+MxJhniJTQUOnQAIK2ykokTJ/L7778by61YscJrU0K8CHNLVBQEBjbcbteF4yJepk2bRlJSEsHBwQwdOrTKybEyc+ZMbDab17/g4ODjsZsKhUKhUDR9dPGSWVFhTNqyZQt5MpD3lluge3fWRIq6LT179mTq1KkA7Nixw2tTIttIiJfG4jKC4yBePv/8c+69916eeOIJ1qxZQ9++fRk3bhyHDh2qdp3IyEgyMjKMf3/99VdD76ZCoVAoFCcHesBuelGR1+Rly5aJNw89BFu2sFkPxh04cCCjR48GYOfOnV7rWC0vzUq8vPzyy9x0001cd9119OjRgzfffJPQ0NAqfjUrNpuN+Ph4419cXFxD76ZCoVAoFCcHBw8CsEsXMT179gQsriOdXbt2AZCcnEyXLl30VQ/qRekEQrzEAhAb26B7XScaNFW6oqKC1atX8/DDDxvT7HY7Z5xxBkuXLq12vaKiIjp06IDH42HAgAE8++yzxsn3pby8nPLycuOzPOkulwtXPXeNktur7+02JZr7OWjuxw/qHIA6B6DOARzHc6BpZpBtLXD89Rd2YEtxMQCTJk3ioYceYsmSJV77Kq0sSUlJhIaGEhcXR2ZmJlu3bmXAgAEAZGVlAeJ9y5YeXK5KY/36Pv66bKdBxUt2djaVlZVVLCdxcXFs27bN7zrdunXjvffeo0+fPuTn5/Piiy8yYsQINm/eTNu2bass/9xzz/GkTx8GgHnz5hEaGlo/B+LD/PnzG2S7TYnmfg6a+/GDOgegzgGocwANew5ar17NwJdfZt0dd5AxYkSt1hm7bRvhQCoQExNjxI0uXryY2bNn43CIzKHNmzcDkJmZyZw5c4iJiSEzM5MvvviCg7r1Ji0tDek2KijYzZw5W6p8X30df0lJSa2XbXRF6oYPH87w4cONzyNGjKB79+689dZbPP3001WWf/jhh7n33nuNzwUFBbRr146zzjqLSD0Yqb5wuVzMnz+fM888k4CAgHrddlOhuZ+D5n78oM4BqHMA6hzA8TkH9l9+wVFczKCsLCrHjz/yCh4PzpwcQIiX3r17c8stt/DYY49RWFhI+/bt6du3L8XFxeTm5gJw9dVX06JFC2bNmsXWrVsJDw9nvP5dQlAI8TJ0aCfGj08yvqq+j9/qrjoSDSpeYmNjcTgcZGZmek3PzMwkPj6+VtsICAigf//+hm/Ol6CgIIKCgvyu11AXU0Nuu6nQ3M9Bcz9+UOcA1DkAdQ6ggc+BHlRrz8rCXpvvyMyEigo8NhvpmsY5KSlGpu8vv/zCqlWrGDRoEKmpqYCwzLRu3RqAlJQUAHbv3k1AQADl5eUUFRUhY17i4hwEBDiqfGV9HX9dttGgAbuBgYEMHDiQX3/91Zjm8Xj49ddfvawrNVFZWcnGjRtp06ZNQ+2mQqFQKBSNE5mZ62MEqBZdlOQEB+NGhGIAxpgrK+1ag3UlMmhXpkubTRkbX7ZRg7uN7r33Xq655hoGDRrEkCFD+L//+z+Ki4u57rrrAGGuSkxM5LnnngPgqaeeYtiwYSQnJ5OXl8fUqVP566+/uPHGGxt6VxUKhUKhaFwcpXhJ0wN8pXgZOXIkAIsWLULTNL/ipWvXroAQL5qmGX2N7PY4PJ5mJl6uuOIKsrKyePzxxzl48CD9+vVj7ty5RhBvamoqdrtpAMrNzeWmm27i4MGDtGjRgoEDB7JkyRJ6WPsxKBQKhULRHLCKl9pkHeniZWdZGWCKl1NOOYWAgADS0tLYs2ePX/HSuXNnbDYb+fn5ZGdnG+JF00RPgGYlXgDuvPNO7rzzTr/zFi5c6PX5lVde4ZVXXjkOe6VQKBQKRSOmstKIeaG8HAoKRI3+mtDFyz6Ph8DAQJKSkgAICwtj6NCh/PnnnyxYsMCveAkJCaFdu3akpqayY8cOXbyEoWkiW6kx1XlRvY0UCoVCoWiM5OSApT9RrVxHunhJRQgTmRYNcPrppwPw22+/+RUv4O06EqnUwtwSHAxhYUd5HA2AEi8KhUKhUDRGfNvo1FG8dOvWjSVL4KefxKwxY8YA8Msvv7B//34ADh5MYfduc3UpXv744w/dC2IG69ahTl6Do8SLQqFQKBQnCpcL/vUvsHR2NjhG8ZKQMICxY+Hcc2HbNhg2bBjBwcFkZWWhaRqhoQO55JJoRoww2iEZGUczZsygsLCQLl1EYbzGFO8CSrwoFAqFQnHimD0bnn0WHnyw6ry6ipeyMmOdVGDPngsoKxNxvl98AcHBwYywVOlt2fJCNM3GoUPwn/+IadLyIrn88tuBxhXvAkq8KBQKhUJx4li3Trzq5fi98BUvvp990V1BxTYbuUSxaJGZpfvFF+JVxr0ABAQMM96/8grs2+ctXi6//HKio8VnZXlRKBQKhUIh2LhRvBoF4SzU1fKiu4z+0jTgNkpKnHTtCgEBsHmz+CfjXgBKSroDEBkpkpkeeUQ0aWzTpg1hYWE8//zzRrKTEi8KhUKhUCgEUrwUFkJFhfc8KV5kO51aipddBGOz/ROAxx6DcePE7C++gMGDBxMWFgYEkJ0tKte/954Ixv30U1i71smKFSvYtGkTSUlJZGWJdZV4USgUCoVCAcXFsGeP+VlvqGggxUvv3uK1luLlC65G01qTlAQTJsAVV4jZn38OTmcA1157LaGhg3G7HURHw9/+BhMnimXefx/atm1r1IeR4kXFvCgUCoVCoRB+HE0zP/u6jqRyqKN4WcNYAG6+GZxOuOACCAqC7duFoee///0vr776JwADBgiry6WXik388ov/XVCWF4VCoVAoFKbLSOIrXqTlpVcv8VpL8bKffgAMGSImR0bCOeeI959/DjabjTVrRNGWgQPF9DFjwG4XAictzdykinlRKBQKhaI5k5cHTz4JBw6Iz3UVL8XF4l91pKZSSDiFiKq5ffuas/7+d/H6zjtQUgKrV4vPAwaI16goU+xYrS/K8qJQKBQKRXNm+nSYMkX4c6Bm8VJeblaO69xZ1OeH6q0vmoaWmsoG+gB24uNdXnEqf/sbJCUJMfL227B+vZguLS8AZ5whXqV4qagwd0HFvCgUCoVC0RzR67Awd66wvmzYID737ClereJFmjycTmjRAuLixOfqxMvhw9hKS1mnu4wGDPAe3p1OmDxZvH/0UaGNIiOFLpJYxYvHY+6OwyF2oTGhxItCoVAoFMcDqQY8HlHSNjtbRMueeqr3fDBdRq1bi2WOJF7mzgXgd0O8OKosct11YjPS8zRggIhzkQwbBqGh4qs3bTL1U8uW3ss1BhrZ7igUCoVCcZJiFSf/+594TU6Gtm2rzreKF6hZvGgavPoqAH/o4qVfv6qLBQfDvfean60uIxAZSaNGife//NJ4411AiReFQqFQKI4PMnUHoLJSvPbuLUwbUDvx4q9FwLJlsGoVpfZAMhBp1f7EC8Ctt0J0tHjvK17AdB3NmmXGvjS2eBdQ4kWhUCgUiuODFCd9+pjTjiBe3DExTJs2jdLISDHdn+VFt7p82GokEExwcAUdO/rfhchI+PhjuPNOEcTrixQvf/xhNmtMSKjFsR1nlHhRKBQKheJ4IMWJjJyFI4qXZXv2cOeddzJPpgf5ipf9++GrrwD4n0s0YuzSpaTGGJXx4+G//xVuIl969RICJiICBg2Ca64RLQYaG84TvQMKhUKhUJw0bN0KbdqYvhmJ223mHY8bB6efDmvWwMiRZkdpP+Jlgz5vS3Y2F0JV8fLyy1BZiWfkSNb9mQTAoEFHP7Tb7TB//lGvftxQlheFQqFQKOqDLVuE6eL0082YFonsW2SzibzjOXNEunRcnGl5yckx2wXo4mVdejoAm2T0rFW8vPACvPIKADvOOw8QVelGjAir90NrbCjxolAoFApFfbBkiUiDXrsWvvzSe54M1o2OFoVTgoJEXjKY4sXtFt2lwRAvB9xuANZL60xmphA4Tz8NDz4opj3xBK/s3gN6plH//rb6P7ZGhhIvCoVCoVDURHo6vP66KSyqY+tW8/2UKd7WF+kS8pe6ExIi/lmX08WLzC1K93jEm/x86N8fHn9cfH7mGUomT+aTT/8EYrHbPUbNu5MZJV4UCoVCoaiJZ56Bu+6CGTNqXs4qXrZvh08/NT9LUSKtLL7ExJjLaVoV8ZILeJx6LMv69RAWBq+9Bo88wrfffktRkWhMNHCgzegkcDKjxItCoVAoFDUhy/rrXZurRYqX8ePF65NPClcQHFm8WDOOCgtF/X4gC2ilV4nb0727iJF56imxL3fdBcB7770HjAPg7LNPfpcRKPGiUCgUCkXNSOHh2/XZSkkJ/PWXeD9tmihLu2sXfPutmCZjXmojXvbtA4S1pdxu5/LLLwfgFZmZ9NhjhqVm3759LFiwEDgLEIlMzQElXhQKhUKhqAmZKWStkOvL9u3C3dOypWjfLCvAyeaLdbG86DVdNgK9evViwIABAOzctavKau+//z4wCIghKgqGDq31UTVplHhRKBQKhaImaiNepMuoe3fxKkvcSmuMLl5eePddHn/8cTwyAFfiR7ysB4YPH06XLl0A2Llzp9cqBQUFvP7660iX0RlniO7RzQElXhQKhUKhqA5NM60mdREvHTqIVx/xsruggKeffpq//e1v5ObmsmLFCt566y1yZEncw4cNa80GvMVLamoq5XosDMDUqVPJzs4mOPhioPm4jEBV2FUoFAqFonqKisyg26MRL3r8ihQvMmrmu+++o2XLlmh6UTpXx47cqS+nrV+PDWF5eWD4cOLi4ggPD6eoqIg9e/bQvXt3MjIyePnll4FoKir6Ac1LvCjLi0KhUCgU1WEN0s3LA5fL/3K+4iUpSbweOCDEjy58DgMvvfQSbdq0QdM0IiIiAFgvM5q2bMF26BAeYH9UFMHBXXj5ZRtJSSKYRbqOnnzySUpKSuja9XY8Hhvdu0P79vVzyE0BJV4UCoVCoagOPd6lnECvz1643aCLCi0lRVhT4uIgMFAUqjtwAM0iXq655hr27NnDjh07OHz4MEFBQWRIUaTHu+wEohPuoW9fG/ffD+Xl/xTTd+5k27ZtTJ8+HYAuXUS6dHOyuoASLwqFQqFQVE9ODq9zBxEUMo8z/adL794NLheekBAShw0jISGByydMID8qSszfuxdycwHQWrSgZcuWBAcH06VLFwICAujRowe+W72b19i6dQp5eeJzWZkom7tz507uuusuKisrOeecv7NoUTwA55/fAMfeiFHiRaFQKBSK6jh8mM+YgItAFjHKb9yLbds2ADIiI8nIzOTgwYN8+eWXrJDNFDdswKa3Cojr0aPK+r179/YSLzvows/chc2mcdVVYlp2dgIAn332Gb/88gtBQUH07fsaRUXQsyeMGVN/h9wUUOJFoVAoFIpqcGXlsZqBAGQSV6N4WZqfD8C///1vLrnkEv6SC6xeDUAR0MmPeOnVq5eXeFmvd4fu0aOYd9+FgAAoLQ0E2pOvf8f99z/Mxx+LPkn33iuaVTcnlHhRKBQKhaIaVq51U4ZomniQ+BrFy9qyMlq3bs0DDzzAXXfdZYgXbc0aQMS7pKSkVFm/d+/e5AGy8ssG+gAweHAwgYFmDDD69KSkJLp0eZi0NGjdGiZOPPbjbGoo8aJQKBQKRTX8sSHEeF+d5YXt2wHYClx77bUEBgYyZMgQDjgcYv6WLYApXjweUcpl82Yxu1evXngQ7QAA1tAbgP79RTWTPkKzEB5+CgD/93+v8vrrIoD4zjtpFo0YfVHiRaFQKBSKalie2sZ4X53lRduzB4A9wI033ghAcHAIJIv0ZpteTXcr7XjnndNo0wb69oV+/USGdWJiIlFRUYbraK1uYektNIwhXoYNu5nvvvuO2NgLWLVKiJZbb63f420qKPGiUCgUCkU1rMvtZrzPJI7KQ1le820uFw49fTppxAi6dOnChg0wfDjM2L6YB/kPHmyk0ZZ/sohZs8I5dEis63bD55+DzWYzgnYLCSeDToApXvqKEBjS0mK44IIL+Owz8fmKK0T/x+aIEi8KhUKhUPghNxf2ujoZn10EkplW5LVMsJ4CXQ5cfNMdPPQQDBgAy5eL+S/wIBcxi9NZQBYdSU6GhQtBL9PC11+L1169epEKbEakRMfElBEr4nENy8vOnVBcbK6jN5tulijxolAoFAqFH1auFK+d2UWIHpGSkV7ptUyQ7kZaT2ten/Y3nn9e1KW75BKYOrWEQMqZzQXsogutA9JYsABGjRJNp51O2LQJduwQQbsPAPfpLqNevTTjO+LihIXF4xGiJyMDoqJg7NiGPweNFSVeFAqFQqHww7JlQkAMZTnBZAJwKCfQaxlXairr6Mv5rGLVqmAiI4Vl5Kuv4P77Q3kjeDxtSKczu3h++KO0ayfWa9ECTj9dvP/6a2F5SQOW6MG6gwebUbg2m2l9ee458XrBBRAU1DDH3RRQ4kWhUCgUCj8sWlQGCPESGlYIwOHCUK9lKlNTuZ73OEQ7unWDFSuEVUXSu8MB/qIDW+lOt94RXutecol4/eYbIV4EQqX07etduEXGvWQKDcWllx7jwTVxlHhRKBQKhcIHTYPVK0Wq81CWExpdDkC2KwYqKozlyg/ksY5+APzyC3Tr5r2dsF69CMBNAO4q1XUvugjsdli1CgoLY2jTJgF0y4sM1pVIywtAeDicddYxH2KTRokXhUKhUCh82LsX8gsDCaScFNsGwluJdOdM4rz6G6UfjEXDTnRANm3bVt1O4vDhxvsEqwJBFJg79VTxXlhfxgEx2O0eS2E6gXXV889vnrVdrCjxolAoFIrmjcslcpYPHjQm6XXl6M5WPGFO4kT/Q1HrxSJe0vI7AtA59oDfTUdLfw8QnJhYZb50HU2dCv37vwRASoqtSjxLjx4ga941d5cRKPGiUCgUiubOt9/ChAlw//3GpN27xWsyu6iMiiIxUVS79a2yu69CmEhSkr1TqA2Sksz3LVtWmT1pkij/n5EBL7zQAoA+fao2KgoKgocfFvE048fX5eBOTpwnegcUCoVCoTih7N0rXvUeRQC7donXzuzGERtLhw7CFCKq7IqKumgaOzzCsjJgWDV+nKQkGDhQBKpERFSZHR0NixcLa8qCBWKab7yL5Omn63JQJzfK8qJQKBSK5o20pOzfb0zatUvEuCSzi6DERDp3Dge8LS85+9LYrGcHjb6wg/9tO52iYMxvv1Xb+rlFC/jpJ7jtNmjTBi68sD4O6uRGiReFQqFQNG+keMnMhHKRVbRtmxsQlpeQhARSUqLFIsThPijq+y/7YTulhBJKMX2HxVa/fZutWuEiCQyEN96A9HTo2fPYDqc5cFzEy7Rp00hKSiI4OJihQ4eyYsWKGpf/8ssvSUlJITg4mN69ezNnzpzjsZsKhUKhOBkoLBS5zrUkT/qIANLTqayE/ftFVEVndmOLjaV7dxGvUomT9D15AKz8vQCAbrZNRjCt4vjQ4OLl888/59577+WJJ55gzZo19O3bl3HjxnFIdqbyYcmSJfz973/nhhtuYO3atVx00UVcdNFFbNq0qaF3VaFQKBRNnOxffsEVFcW6089g61ZYv/7I6xT99Zf5IS2NtDRwu+04Kact+yEmhpAQJyEIC83+VGGd2bpVxMEkB2+rsk1Fw9Lg4uXll1/mpptu4rrrrqNHjx68+eabhIaG8t577/ld/tVXX+Xss8/mgQceoHv37jz99NMMGDCA119/vaF3VaFQKBRNnGXvfMPftO84deE39OgB/fqJgNiacOblGe8r9uwxgnXj2IsDD8TEABDmFCnSMqN6z0GRP90lJrU+D0FRCxo026iiooLVq1fz8MMPG9PsdjtnnHEGS5cu9bvO0qVLuffee72mjRs3jlmzZvldvry8nHLdRwlQUCDMeC6XC5fLdYxH4I3cXn1vtynR3M9Bcz9+UOcA1DmAE3sOystFCElgYNV5v2zowg+c7zXt118rGTLEU+32QktKjPcHV65kR8kkwEEHhIpxR0WhuVyEBeeRXQRZh524XC72FXUGoFv7nGZ5LdT3NVCX7TSoeMnOzqayspK4uDiv6XFxcWzb5t/MdvDgQb/LH7QUD7Ly3HPP8eSTT1aZPm/ePEJDQ/2scezMnz+/QbbblGju56C5Hz+ocwDqHMDxPwelpQ7uvHMssbGl/Oc/f1SJg92eHg3AzbzFXP4ilWeZN+8gffuu8rs9m9vNBZVmp+g9v//OT6k7gO50RhR7WbZjB4fnzCHAKdRSZkEoH3/8K3mes7FTSVSL/c06NrO+roESi4g8Ek2+zsvDDz/sZakpKCigXbt2nHXWWURGRtbrd7lcLubPn8+ZZ55JQEBAvW67qdDcz0FzP35Q5wDUOYATdw6WL7dx+LCTw4dD6N17PO3be8+fUrEMEP2IMkgnFUhNbcH4aiq7Fe7ahQb8yLkMZylRhYXk54vMoe62PaDB0PHjoWdPXmjzK7vyIN/dkujo0QB0ZQdDzh9LbDOsHFff14D0nNSGBhUvsbGxOBwOMmUbTJ3MzEzi4+P9rhMfH1+n5YOCggjy0xc8ICCgwf6gGnLbTYXmfg6a+/GDOgegzgEc/3Ngja1dvTqAzp2956eViwk92EJggpvZ6bB/fzAulx1/xviMjRv5gLv5J//HBXzHf7ImssctzDm9tJ0ABMTFQUAAMQkO2ArZnlZ89F4xEMwA1hDbp0+zvg7q6xqoyzYaNGA3MDCQgQMH8uuvvxrTPB4Pv/76K8MtzaqsDB8+3Gt5ECap6pZXKBQKRfNBlu0HWLbMe15eHhzWEgDRk2iYKw04hKbZ2bzZ//b+WreD//AQIKwvrpIIMjNFQbquuttIBuzGJYkqun8ykm9mi1L+/+A1SEg49gNT1IkGzza69957eeedd3j//ffZunUrt912G8XFxVx33XUAXH311V4BvXfffTdz587lpZdeYtu2bUyZMoVVq1Zx5513NvSuKhQKhaKRU5N42bhRxK60JY0oCmiXlwtsAGDDBv/b+3JuezIRlv1KnHzCPbjdwYCHJPZBWBiyS2KHDkK87KEzGnYu5UsGsQJ84jQVDU+Dx7xcccUVZGVl8fjjj3Pw4EH69evH3LlzjaDc1NRU7HZTQ40YMYJPPvmERx99lEceeYQuXbowa9YsevXq1dC7qlAoFIpGjlW8rFkDFRVm1tGKFUVAFD0QLaGDXC6iWU8eZ/it91JaCt9uErEq/ULWsq60P68jHpSDSCWICmhphix06WL2JnLg5hn+RXFEBCHOJh8+2uQ4Lmf8zjvvrNZysnDhwirTLrvsMi677LIG3iuFQqFQNDWs4qW8XBShGzxYfF67VpTN6MEW0QSxsJBkNrAK/5aXd96BfFcsHdjH/wY8y2mLP6YY4TJqowfrMmiQsXy3btHG+5t5m67sJDcuuZ6PUFEbVG8jhUKhUDQJiovNAnHDholXq+to61bx2oMtcOqpAAxGmFzWr/fuGFBRAc8/L94/zHMkdQlnbMTvxvy+Tr3w3OjRxrQePWKx2/cD2dze7QsAyvR4GMXxRYkXhUKhUJxw9u+HiRNh48bql9mzR7y2aAHnnCPeL19uzt+3JwSAnmyGUaMAGMtWwEVenlfTaD7/XDRBjCada5lJdJcuTOy20pg/pFKvRTZmjDEtKMjJihVuli4tpNe3/8MzZgx75Y4ojitKvCgUCoXihPPSS/Dpp/Cf/1S/jHQZde5c1fJSUAA5eSImJdm23VhgEBWAECIy7kXT4JVXxPsLeZ0gKghOTOTS0cW0IAeAFM8OiI2FHj289mHgwCSGDesI3btT+fPPZPXvf2wHrjgqlHhRKBQKRYOxdClMmwZud83LrVghXmvqwWuKF41BgzzGtKws02XUhnRCQ8uhTx8AOgABPhlHixbB2rUQGOjmKt4SE2NjCemUyDvcxPW8y7n8KKw3djVMNkbUr6JQKBSKBuO66+DOO+GeeywxJ9OnQ9u2hppwu4WYANi2rXqhI8XL3Llv0LVra2JjswDhOtoiEozowRZcEREQHQ0dOgDQ1hL3AqbVpU+f9STrlhZiY6FdOy7hG97lRpFpZIl3UTQulHhRKBQKRYPgcmF0aJ42DV57TZ/xxRdw4AD89hsAmzeLtGUQgbTWjCIrcnp+/moOHz5MdvYPADz2WB6y129PNqNFR4sPySITqINuefnxR5g0CWbPFrMTEr4gVm48NlYIKiuWeBdF40KJF4VCoVA0CGlpYOl5yD//CT/8gIiUBSgqAmDlSu/1qnMdmaJmN506daJHp59xUMy6ddG8846Y04MtOFq2FB90EdOJpUREFFFcDB99JCxA48dD/sGFemI0huUF62efeBdF40GJF4VCoVA0CFJsdO8ON94oRMPdd4PnQM3ixV8pf7fb2tdoN2PGjOHHsZGs4FQibenGcj3YInoRAURFARBHAVdf/SQ//wz33QfnnQdTp2rk7BS9izSnEyIjRRuAYFFFl9GjqdKyWtFoUOJFoVAoFA2CTG3u3BlefRVCQsS09XkiFuXANpEFZIoXkTrkT7ykpgoBY7dXAOkkJyfT7tAhBrCWZ7Uh9OhRQEt2MJDVBMtGvrrlJQrIykrlrLPgxReF2yg4eC/23FyxXMuWQqjYbKb1RcW7NGqUeFEoFApFgyDFS6dOEBpq1mb5mksAyNq3j7Iya22XGYB/8SK3FRR0ANBITk7GsWMHAP05wOjRj/AEKYRSil0WjtMtL9HAQVndTmfBggVGvIutVStzxh13wPDhcPnlR3XMiuODEi8KhUKhaBCs4gXgEqFZDPFSmZfHunXCohIUlAf8BMCOHSLY14p0QbndQrAkd+hgRAO3A77++itaoqcztRAdn62Wl4yMDK/t/fbbb97BupK774YlS8AqaBSNDiVeFAqFQtEg+IqXvn33Y6eCbXRnC92hsNBwGQUHbwTSgEJcLtDDUQykeHG5REGXZIfDUDgJQHZmJi3kwlK8WCwvVvGiaZqX5cVLvCiaBEq8KBQKhaJBsMa8ALz55vN0YD4A3/A37CUlhnhxu/VcZ4TPyOo6qqgAs4fvHuLi4gi31Pp3IARMtJzgx/JSVFREkR4gvH37dg4ePEi8wyGWU+KlyaHEi0KhUCjqnZwcyMsT75OSIDMzk3feeYfBfA0I15GjrNyorFtc/Ju+plAtMl3a44HrrxdBvUFBLuBHkpOTRTU7C+2gWstLjJ41JK0vv+n1ZXrJwF4lXpocSrwoFAqFot6RVpc2bUSw7ty5cykvL+eSqN9x4GYd/TlL+4vt2+Uaq/RX0/KiafDgg/Dxx+B0woQJXwF76Ny5c+3Ei255idZL/Mug3QULFgDQRS6nxEuTQ4kXhUKhUNQ7vvEuixcvBmBQRDljEJaPTNoSEuLhssv2A9n6mkK8rF0LV10lUpsB3nsPbLZ5AN6Wl7AwANpTveUlSu9LkJGRgcfjYaHug0qUNV2UeGlyKPGiUCgUinrHV7wsWbIEgDiXi/9xG0/yON9yNp999gsXXLDQsuYmY/1PPgGHQ7QVmDQJdutRu8lWy8uoUQB0AwLlJnwsL8EeDwEI8bJp0yays7MJCwsjqqJCLKfES5NDiReFQqFQ1AsejwiuBW/xkpuby2Y9Ajc0P59kdvM4T3MRP3MoYy9paWl0BD4G+pBOQEAhIPoq/vEH3HWX2NYuPTU6JTYWcnNFUbmxYwE4NTISAM3hMKwx6NPATJeeN09Yb0aOHInt8GExU4mXJocSLwqFQqE4ZioroV8/6NtXVP23ZhotWyYq5w7q1AlbWZnXeof27CEtLY1JwETgDiAh4WkeeADWrRP14gCKi4uNgNtk2XY6KQm6dgWgi66abC1amGX9HQ4IF92LohCuq6eeegqAc8ePh2zdVaXES5NDiReFQqFQHDMZGaJS7rZtohWArMvSqZPpMjqrVy8xMSaGSj2INnvfPtLS0tBbKdIayM9/hxdeMLw+gOkyiomJIeLAATExJQXatwcwRVGLFnghg3aBP//8k8LCQk477TRuu/BCKC8XAichoV7OgeL4ocSLQqFQNFP0ONZ6QeoJgKlTRS8iEOJFBuue0rGjmJiYiDskBIDctDTS0tKMYNtYIC8vjzyZZ60jXUZewbrdunl3goaq4kUG7eof4+Li+Oyzz3Du3SsmJCVBQEDdDlZxwlHiRaFQKJohTz8NbdtaOzUfG1bxkp8v4l+CgyE21s3y5csB6CvdMwkJaHpcSmF6OmlpaUaBuTi9cNxfPjvmV7ykpAjLioxxgRotLw6Hg88//5w2bdqYJXy7dDm6A1acUJR4OUYeewxGjoQrroAHHjBabSgUCkWj5ssvIT0d9PjVY0aKF2tLoE6dYMOG9ZSUlBAdHU0bOSMxEVtEBAD5Bw6Qk5NjiJdWerzKvn37vLZfrXix2QzXEVCt5eXOSZOYPXs2o/TsJCVemjbOE70DTZkdO+Df//aetnEjzJ17YvZHoVCYlJZCUBDY1SOaX9LTxatZJO7YkOJlwgRYvhxWrPCOdxk+fDh2+aUJCQToFpEgPfi2pd0OHg+Rbjd2qoqXFXop3u4dO4J0+aSkiNd27WCr6HlUneVlTP/+ZltrMJ80lXhpkqg/62NghujezvDhMGWKeP/nn6JDqkKhOHFs3izGsPvvP9F70jgpLweZJexTqNagtFS4lGobFyPFS9u2MG0a9Oolyvob8S6nnGIqpsRE7LpFJFxfP0ZXmXZEsTmreNmxYwfr16/H6XRydpcuYqciI6F1a7FALSwv+MTQKMtL00aJl6PE7YYPPhDv77tPuI9atIDiYlEZUqFQnDhmzxYD9A8/nOg9aZzoVfIB/5YXTYMBA0Qsa8uWMG4cRg+i6jhwQKicjz9+gQEDPGzcCBdfDEuXioaLw4cPNxVOQoKRwhyhrx9lUUmxeIuXzz//HIAzzjiD6JwcMTE52UyJtgbtWlOUrJ/z881pHo9peUlOrvnAFI0SJV6OknnzxENEy5Zw/vnCNH3KKWLeH3+c2H1TKJo7q1eL1z17wOU6sfvSGJEGEBAeGFlYTpKfb1pkcnPF/e6ZZ2reZmqqB4ANG+awVXfhZGRkkJqait1uZ8iQIeYXW8RLOCJ+IaSy0thWS7zFyxdffAHAFVdc4Z2DLbGKl9pYXg4cgLIy0TApKanmA1M0SpR4OUqky+iqqyBQr0l96qniVYkXheLEsmaNeK2sNMMjFCZ6rTdAnCOpByTSMhMZCW+/Ld77hKB4oWmQkaFbQThgxLnILKNevXoRHhxsbjgxEfSA3XAwgnUlVsvLli1b2LRpEwEBAVx44YVV+w5AzW4jf5YX6TLq2FEIGEWTQ4mXoyA7G777Try/7jpzuhQvf/5Zv/UTFApF7cnNNcc3MMcphYnV8gJV416kxmjTxrQoy7ot/igogJISOZwcMOJcZGXdoUOHwqFDwl3jcIhYFYvbKNpne7LWS25uruEyGjduHC1atPAu3Supq+VFxbs0eZR4OQq++MKOyyV8wn37mtMHDoSQECFuqguCO9nQNPjXv0TLeoWiMeAbc7Zjx4nZj8aMr3jxjXuRlpk2bUxdkJcnRIo/zBovuUCpYXmR4mXYsGHmQvHxXmX7w7F0g9bprPckGj58ODN0M/cVV1whZh6t28hqeVGZRk0eJV6OglWrhHn0oou8pwcGwtCh4n1zcR1t3AjPPgs33FD9jU2haEi2bxdxZ9JVJF8lSrxURYoT3XNTRbxIy0t8PLhcOQQFFQOQluZ/e6Z4EW927tzJwYMHWbVqFaBbXqyKyPLl/txGE844g1atWrF9+3bS0tIICgriggsuEJYb6Qe0ipeQEJHeFBJSNYZFuo38WV5UsG6TRYmXo0AWfvR33Y8cKV6bi3iRgf/NJbNj7lwbt98+liVLbEdeWHFceOUVce3JtGgZrNutm3g9Wd1GlZXw5ptmeZO6IC0vp50mXmsSLx988AHl5eIk1la8ALzzzjsUFxcTGRlJ9+7dITNTzJDixWJ5ifbZXqfISHbu3MkDDzxAcHAwt9xyC5GRkUIAyX5E1jgXEDfdHTtqZ3lRbqMmjxIvR8Fff4mBy1+QenML2rVaW/SEgJOazz+3k54ezpdfKvHSWNA9E/z2m3gol5aXCRPEa1OwvLhc8Pjj8OuvtV/nu+/gttvgzjvr/n1SvIweLV63bfOO07OKl82bNwNCtVQX9+JPvLz++usADB48GLvdbm40Lk686uIlOS6OC6WKkmRnExUVxQsvvEBJSQn/93//J6ZLl1GHDlUDbaOjRZEZX6wBu5omrDdyO0q8NFmUeKkjbreN/fvFe3/iZfhwkTb911/VP6WcTBQWmu/nzj35XUfyt9+9W4mXE8G6dfDQQ6KeEkBRkXBdSl5/3RQrMkQiLQ1KSo7rbnL4sMjErS2ffCJ6DV1+uf99XbEChgyBBQvMab//Ll59M4Vqg/TgyEr5ubkiVk9iDdgVac9CtVQnXuTfBRwgPj4egEOHDgF6vIt1o/p8KV76dOzIlePHi2l6rIt1Z2w2GzZZz8VfptGRkJaXykpx4aSlCetNQEBV642iyaDESx3Jzg7B47ERFGQ+QFiJiIB+/cT7IxV1OhmwipXyclEc7GTmwAFxE921S4mXE8E//wnPPy9ECsCqVeJBWvLf/4rXtm1F5XjpQTiaAf5o2bNHPNj8/e+1X+ezz8RrTo5ZhkGiacK6snIlvPSSOV2v/UZ6et2yG63VdTt3FkYM8HYdmUYSjS1btnAk8WK1vJx77rle84bKQEBf8SIDboqKzHgUaQmxKikr/jKNjkRoqGmlycszXUadOqk06SaMEi915NChUEDcnKrrmdKrl3htDhlHvpaWpuw6stTI8oummU+Y+/ap4mdHg6aJ0vHS1VMXysvN9aRIlp/PP188YMvfZOBAUXy1a1fx+Xi6jr7/XozHv/1Wu+UPH4ZffjE/v/yy97U4b54QLiCsLS6XsOrIrCqXq/qx3h/S6hIUJMSdjA2yihe5TGBgDrm5uUjxsnev/z8Sq3gZPHgwrSzdGasVL7rlhcJCU7zIQMLqDshfptGRsNm8415UZd2TAiVe6ohVvFSH7BXWnMTLmDHitam6js4/X9zLaqplkZ0NZWXC4uJ222pcVuGfr74SVoRrr637umvWmK6YpUvF7yHFy5gxZowLiDIGYD7IH0/xIl07+fneMaLV8c03ot1Ijx6iYveePWIaCLH39NPmskVFwtq0Zo23eDbFw5GxJv3YbFXFi1UM5eXJm5i42P/6y2LmsmAVLy1btmTEiBEAdOzYkday/1B14qWoSPitwBQUeXn+m8QdjdsIvDOO5IGqeJcmjRIvdUSJF2+kUBkxQhx3RYV48mxKlJSIbJV9+2DixOoba/rGMMkHuMbCr7/CpEl1ewo/3sycKV63bxdjVl3480/zvccDP/1kipdhw0QTQEmPHmU8/fTThIXJ1N2j3+e64HbDokXmZ5mZWBN6DTYmTYI77hDvp04VwmXhQli8WFhJZCbjggWmy0hSF/FirdAPVcVLVpb4bocDDhxYr68lLv6MDIeXmw6E2NHDW4ADxMTEMEZ/mhklg2rAzDaqyW3UqZPZr0imMlo5GrcReFte5EXTv3/dtqFoVCjxUkcyM+smXk72SrtSvERGwtlni/dNrTGltRrr4sXwxBP+l/MVL40tBfeRR+Cjj2DWrBO9J/7JyBCWOcmmTXVbX4oX6ZF4/XUxHgYEiHFo8GA44wxhUZg9+0Eef/xx5s9/Azh+lpe1a70tjzWV1Aex/9K9dPnlQrwEBws3Ub9+ZgXvG280Y2j8iRffonM1IS0vvuJFPmxZk4K2b5d52OlAJW633dAg1u2J+1wFkEWLFi24/fbbmTFjBlOnThULFRWZUdY+2UaUlwvFBML0FBMj3vuq8MJCUyXV1fIixcvBg2YuvVSDiiaJEi91RFpeOnasfpnOncVTS1GRdw+RkxGZbRQZaQq6puZOkSJEPgg+95x3DIKkMVteiovNFOHGes19/LF3cK01S+hIeDxCWAI8+qh4lQHx/fuLAd9mE+Lorbd+5IMPXgPgr7/mA8dPvPjGuRzJ8vL11+LYBg8W43Hr1nDXXWLehg1i/YAAmDwZTj9dTF+82BRy8kHpaCwvstxKz57iddcuKC319u5s3bqVMOBBKnHqadC+f9/p6TJ4/SCgERMTQ0BAANdeey2xsbH6LH2jYWGmaJGvYP5xRUcLAQNVxYssThcTY4qR2iLdRr/8IkxF8fE138QVjR4lXupIbdxGgYHmg8HJ7jqyWl5k1mFTEy9ShJx3Htx0k3iKfPHFqsvJ+2twsNtrvcbAsmWmu0uOE40JTTNdRtJrUBfxsn27CGwNCYGbbzatLyBcRpLc3GxuuukG43NlpbAcZGV5F1htKMxUZqHqd++uxgep8+WX4lWmdYPIptq5UwQlv/SScI+1by8sJG3aCENFZqZIlLngArHOsbiN4uMhNlaIqC1bTPEbHy+aIl4G/AfoXE3GkfndIpo9RlpOrPjGu4BQZUFB4r20vLRoIXYGqoqXo3UZgSl2pOlv5EjTPaVokijxUgcqKiAnJxg4chf15hL3cjKJl+Rk0zTv7xikeOnR47DXeo0Ba1FEX7N+Y2D1ati8WVhIHn5YTKuLeJGWhn79yvjss5mMG2eKgmHDICMjg7fffptzzjmHzMxMevTowfjx44EiIiJEcE1Du/lcLmtcjki727695mIv0sU6bpw5zWYT1+J558G998LYseZ0aX0B0VdNxpz6Ey9r1oh1+/YVRoZx40TAs2+VfpsN+vQR79evN3VGTEwFGRkZSLmR6CNeMjIgPz/QYnk5wN9tNsL9ldr2J17A2/oCwkJSnXg5mkwj63bBDAyW3SYVTRYlXupAaipomo2QEA0ZQF8dzVm8HDwong6bCtY2J/KG7i+GQIqX3r3FTXXPniOnVx8vrMGsjdHyIq0uF19shhps3Fj7mDB5fDk5s7nuuutYs+ZJY96iRc/Ttm1bbrnlFlatWkVwcDAffPCBETQaGLjP+L5jITNTCAKr6ysvT8Qa/fqriFMpLganMxcQA/i+ff6zc+S6Mhupth4Mq3gZPhwSE8V73+vV4xFWxAULhPtp3z6Rcv3RR+ayP//8Hq1bt2b79u1Gg9kNG8zrx+EQ1pC2usDoYBEvmzdDt25OrrnmHJ54QgwjDvbzvqZhmzTJFAkS32BdSV3ES31YXiRKvDR5lHipA7ItQIcOR7Y4NkfxEhsrnqzBWnGz8WNtMCtN6fn5ZnyhRB5Tt245BAVpuFy1szJt2AC33NJwgcwul3cAZ2O0vHz3nXidNAmKilZit2scPlx7oSXFy6FDIod4y5ZXCAzcT2TkZt566yE8Hg9DhgzhmWeeYdOmTQwcOJDhw4cDUFYmApgWLvS/7bIyEevhj8pKePJJ4bKJjxf1Yz780Jz/8cd2nntOBApLq53d/gewD4D09IBqj0nGw8TGilCQmtiyZQtjx44lKmq1Ma1Pn2KmTXsEqGp5+fxzIbQiIkQm3eTJYvrLL5vL/vHHF2RlZfH222/7tby4XOLibq8P/B0tLQJefNEsG1BUJF5D2U6APGm+DZdqY3mx28UOVydeZODSsVheQBStk5VEFU0WJV7qgLzZJCUd+XGxOYoXm820vjSV1gilpea+JieLe6ccSKyBrx6PedNv1arEeFKujeto6lR4+21R3v2pp+q/uN3atSLdWxZNbGyWl/37xT8xNq1l9OihBAaKgXHDhiOvn54uHrrtdo3c3B+x2+3ExoZQUZFEQUFvwsPD+eKLL1i+fDmPPPIInfUn84EDBxIQEEBxsahot2BBVUtPRYX4XTp18l+T5euvYcoU74Bfq4vOOkZLIVtR8RMgbhYFBSHVCiOZiXQkFzTA888/z4IFC3j55X8wdKj4e9u16x1+/PFtQIzz0tpZUQH/+pd4P3kynHuu+BwZKfZXGkUOHBAdnz///HN69RIWog0bzOu+sFCYJBP0P4iuuuVlzRoRfA3wxBNL+PZbN7ffvoYYZpo77Hvj8+1rJJFR8iAEhs3mX7xomihwA0eX4my1vAwdKuJtFE0aJV7qwL590vJyZPEi0w/T0upez6KpIFuFgHkPampxL9ISLS1HNptpfbGa4jMzheiw2zViYsrp3FlcA7URL3KQcrtFGva4cVSplXEsSKuEbApqzUptDCxfLl779IEFC2ajaRplZaJkbG1cOe+/L147dswHCklJSeGPP/6ga9fODBjQn5UrV3LZZZdVWS84OJj+/fsDS3A6KzlwoGrW0XvviX04eNB/RVzZD/COO+B//xPvrePyjh3invDQQ3DWWdCpUynwNYnkIoN2q/tbkA9DHTpAVlYWo0aN4oknnkDzUVgej4eff/4ZgCVLljBt2k62b/fw5ZevAYex2YRqkaLjrbdEYk58vGinAOL6vukmc5uBgR40TcRuHThwgPz8pTgcorSKFJRZWeLHaaUP9CkWt5HLBSNGeOjfP4tzz9UYOHAdkVh8xUdjeZHWEX/iZdcu4WcLCoLevakzVsuLSpE+KVDipQ7U1E3al5YtzYyIptDV9miwijLZTy0hQZgVmop4sbqMpCvQn3iR1pk2bcDh0EhOrr14kefi/vtFtsxvv5nl3usDaQkYP15sH46f62j16iO7CK2F5BYY6ThihDySeMnKEqnrAP36iXUHDhxISkoK27ZtY/Xq1aRIM6cfhOuojLg4EexpbWxYVgbPPGN+ts4DIbqWLhXZg48+Kh7YwbuM/s6d4qK56CL4+Wf4979n0YEs9gEddOtLdenSpiUXXn31VX7//XeeeuopHn/8ca/l1q1bR6blB/3yy3fZunURe2XqMOJCPXBAWOCeekpMnTLF2x31j3+IEg4A0dHe5qBvv/3UeOCS5Q/27xeWDmmzaI/3H/U995gKPCcnB6/oleosLzWJF9mIyp94kX8w/fsfndXEanlR8S4nBUq81AH5BF0bywuc/K4j6TIKDASHw82NN97Ihx+K0aCpiBdrsK6kJvHStq3mtfyRxEtlpeluuucekUEC9dfAUtO8LS/SKn88xMu2bWJAHz265sBlKV4GDKhgqRGcUzvx8u9/i8G0f3+oqPhA346o/W+rRaqrjHvRtF8Bb4EyfbrpzvKdB/Dqq+L1738XY67sk5SdLdK2S0sdRqNOOW/nzp30BZxA1yOIF3k/SUhw8dZbb1mO+d9mcTfgp59+AqClXv/kgw8+4O233zbma5pQjwcOCCGbnS0Cea0Vh0FYRaWBKiQkV/9ucbF/+eWX9OnjbQ6U4iVMz8GPJg+bbk3q3BnOP9+8D1YRL7W1vPi6jcSBild/4mXwYI4KuW27XUQ6K5o8SrzUgbpYXqD5iJfISI2LL76Yd999F00Td+qmIl6slhdJzeJFvEq30ZHSbzMyxMDudIr79vnni+n1JV62bRP3+JAQEUwqx4bjEfeycKGdykqRwWqtnGvF5TJDFQICVlNRUaHPEaply5bq2zHs2gVviAK5TJ0Ka9eKYNWBAwfWeh+leMnM/BQQVi+PB9au3cbkyXmAaanYvNk8b/v3mzVY7r5bvIaFQbt24v2OHTbS08Vw3aqVaTTYuXMn+uVDci0tL/v2LSQ7O5v27dvz73//G4DJkyfznR7lPFc/uY8//jixsbFkZGTwmWxDDVgtL9L1deaZ/g0UU6YIr0vbtkLMXX/99bRs2ZJDhw4RFma23g4JcQNFdOjQAaduYrUBwQg/6z//CXv27KRAvwnk5uZ6i5e9e81GVJpWu2wjX7eRbH0NZkXCIUOqHlRt6NFDKODrrzfNxIomTYOKl5ycHK688koiIyOJjo7mhhtuoOgIASCjR4/GZrN5/bv11lsbcjdrRXm5WUlSWV4EUrwUFaXzg1HbwcxIaAr4azBbk3hp10789lK87N4tXCfVpfzK89C2rTDZjx8vHv5k9dRjRWbQDB0qLGDH0/KyfLlp+bAYAjh0yBQBGzaIMaxFC9i5cw4gn/b3YrMVU17u33rl8YgB0u2Gc86B3r0PsX//fmw2G/369RMzrrzSTKOphnbt2pGQkEBl5VKCg90cPgyrV7s566xllJZGEx6ezQMPmMkncvCfNk18xahR3vGh1j5AUrzIaSDEi55tb7iNquvELC0vc+cKq8udd97Jv/71L+7W1dLdd99NRkaGYa264IILuOqqq4z1e/XqRXx8POiVb9PTzeth9Gj/56NbNxnT8g4APXv25JJLLgFg//45xnKhoeKPe+jQoV7V/XrwD66+ehejRu2gf//+PP/884Afy4vHYyr73FwzSt23xkRNbqOCAhF97HKZqXpHa3kJDhaRxu+8c3TrKxodDSperrzySjZv3sz8+fP54Ycf+P3337n55puPuN5NN91ERkaG8e+FF15oyN2sFXIQCg52G39bR0KKly1bGmafTjRSvJSVHSI4OJjLL78cLEF9Dd3XqbRUDNqyyujRUJPbyJpt5Gt5ad9e3HcrKmDQILH+vHlVty+vGxnI3LKlaGIJIoX1WPlVPEAbhcxqY3kpLobbboP584/tu63i5YcfxJP/nj3Qvbv4d+CA6TIaOhQWLhR+mXvuuQfQ0DTR3Gj9eu/tapoIkP3hB2Gxev55WKP3PujatSsRERHiZH/yiTDJ1NDG3Gaz6cXq3Njtor/ABRfkkZ19LQCRkc8RGGievwULxO/+3/+i76v39uTf9PbtNg4cCNf3yZxvtbxI8bJrV9X0sqIi07Cwc+d8QkNDufHGGwF45plnaNu2LX/99RcXXXQRlZWVpKSkkJSUxPUWX9D1119Pq1atkOJl2zbTyiW7vEvS0tLYZnmK2q4H7nTr1o0Jejvu5cutA7u4gIYOHOgV/d2Z3znjjKX89tvPVFRUsHXrVkpLS6uKF7lDYK16Z1bUlfhzG0VHm768w4eFSay0VMStqE7QCp0GEy9bt25l7ty5TJ8+naFDhzJy5Ej++9//8tlnn5F+hC5ioaGhxMfHG/8iG4GZLyAAbrjBw8iRB2pdVXrgQPG0vXnzsRfIaozIwD4ooFOnTpx99tlIy0txcdU6VfXN558La/Ls2aaFui6UlZmixHpP9FeoTi7ncu3hhRdeYPfuHfz4o4ghCAkRg7YlTKHKetLdAKbr6FjFi8djWgrk4Fsby8sHH8Cbb4og07o2R5QUFASya5f4Q+jXT+zLG2/ApZeKjJW8PHjwQWu8SzkrdNP/ZZddRlJSEiAsCu+9573thx8W+2eziaJqvXvD6tU+LiOZqwtV4yt8eP55UcSupES0Oz94UD59/IP09Jc5dOiQUfxtwQJ47DFx/Q4dCiNGZPHDDz+wTzeTSCuLcBuFeU3LyckhJyeniuXFn4VNTgsIKAIKuPrqq2mhWx7CwsJ4Ue9PIc/ZOeecA0Dv3r255JJLSE5O5uqrr/YSL/PmCRdlp06mWAYoKSlh6NCh9O/fn7S0NHJycsjW40m6dOnCqaeeSnR0NHl5m4mMFEKruFi4h4b36OG139HAoUOH+FMPtPJ4PGzatKmq2wjM36W6NGnw7zay281sh1WrzHiXQYNMUaNo9jgbasNLly4lOjqaQYMGGdPOOOMM7HY7y5cv5+KLL6523Y8//piPPvqI+Ph4zj//fB577DFCQ0P9LlteXk65pZyr9MG6XC5c9VhQIzERXnvNxfz563C5Wh15BcSDxgUXOPj2WzvTplXy3//WY37sCUKeU5fLRW6uDXEJFRIZGUlYWBhQhtOZi9vdgt27XV4PVqtW2Vi+3Mbtt3vqpa3ItGkOpP4+dMhliI7asmMHaFoAEREa0dFuw7It7psBpKdruFwiIGP/fidg488/P9XTVafx6quvMnw4/PmnjdNPd7Jjh7m8ZN8+O+AgMbESl0v8/uPGwYMPBrBggUZurrtKkdGayM01retr10JOjtj/fv3E/rdqJb4vPd2Dy+XfXfHrr+K8lZTAJZdoLF3q9vqdjoTL5WL7drET3bpp3HNPJdde6+TZZ8X86GiN/Hz4+GMbkZEaYCMoaB1ut5uOHTuSmJjIgAED2LfvVez2fzBvnp3Fi90MGaLx1lt2nn9epMS88Yabv/1N0+NmhEmhb9++uPLycM6ahbyE3Bs3oulBvP6IiIjg/fff54wz7jOsgR3b/YcLtW95az/89ttvnHnm33A4nOzZY2PvXrHPmZkTiYsTsTKdO3dmy5YtJCfbASfbt0NlpThpnTq5cbk0tuqDdQenE9xuQ7xkZQVSWurCabnb7t4t/nY8nn0ATJo0yet+dfHFF3Pqqafyh55KdsYZZxjzP/30U2M5EcQrxIsMJzrtNO/f/t133yVDNyN+8803hgBMTEwkKCgITdMYO3YsX3/9NdHRaRQUdKKsbA8Oh4Oe0tSo0wJIT083xAsIq5jV8qLZbNg0Dc+WLVS6XNj278cJeOLiqPS5J9tCQoxBqDIyEo8+3z5hAo5XX0W77z60oUOxA5UDBxrzGwvW+2FzpL6Pvy7baTDxcvDgQVr7+DedTicxMTEcrMGmPXHiRDp06EBCQgIbNmzgwQcfZPv27XzzzTd+l3/uued48sknq0yfN29etYLnWJlfB3t7//6xfPvtKbz/vsaoUfMIDa25UVtTYf78+Sxb1gnoDRRQUVFhmKVttjSgBbNmrSE93fyt77zzdPbvj+Dw4ZUMGnRsQRk7d0azatUo4/P33/9Ou3Z1K6izYkU8MJRWrfL56adFxvTSUgdwHkVFNr7+eh6BgZWkpwtzyb59YjBZsGABc+aIGIH8/EDgHNLS4Ntv5xIUZIrUVauGAG0oKNjEnDn7AOEWiYs7g8zMMF54YS3DhtWuDfSyZfH85z9DufLKrVx22Q6+/TYZ6Em3bpnMm7ecvLw8fvnFBlzN9u15zJnzR5VtaBr88svZQBDBwW527HBywQWHuP/+VXUSlNu3dwcgMTGN0ND1hIePo6goEJtN4557lrJ4cQLz5ydRUCA2uny58MN06tSJOXPmEB4eDuyjZcufyMo6l3vuyWbixK08+OBpAFx11RbatNmJfopZrLeUrqioYP1TTzGopMTYl30//MBm/Uk9ZssWQrKzOXDaaVX2+fLLu/H55zcCh/i6bTb9l4pB9aOPPiI4OJjk5FPZvj0GTbMRHDyLffuESLDZbOzevZtp06YREdEDGMfu3RAQIIbrgwcXMWdOEQv1gBMZkhrPQRyUU+kJ4qOPfqF1azM9ec6cJKAvlZUi4Gfv3r1kyeaEOpdccglLliwhNDSU4uJi43qzUlxcjAzYlURHr2XOHJGBVFlZybNSVQIzZswwUqxjYmKMbcqso8LCH4B/AJto164da377jdGWbbcAPpo3z8t6/uOPP5KZmWmIl6LERCL276dgxQoWzZlDp0WL6A2kV1ay2ucYEnftQj7ert27lwP6fOeQIYyNjiZ4504jKGq13U6Gn3PQGKjLmHAyUl/HX2L5uz4iWh158MEHNaDGf1u3btWeeeYZrWvXrlXWb9WqlfbGG2/U+vt+/fVXDdB27drld35ZWZmWn59v/EtLS9MALTs7W6uoqKjXf8XFxdqsWbO04uLiWq9TXl6hpaR4NNC0V1911/s+He9/1nPw+ONuTQyH/9Muv/xybdmyZRqgBQfP0UDT/u//zOPNyKjQl9W0++8/9vNw9dWVxvZA0xYtctV5Gy+8IPb/0ksrq8yLjBS/2caNFdru3WLfAwI8WkpKD/0Yg43roLy8QouKEsuvWeO9nb59xfRZs7z37667xHffcEPV767u3w03iGMODfVoBw5UaGedJT6/+KI4n+PGjdNguAaa1rGjx+821q6tMLbxyy8uzekU+3fHHW6trKz210Dv3oc00LQ33hDH9fDD4nieekrsy/795jnp1s2jDRkyRAO0GTNmaBUVFdrcuXM1QEtMHK3Z7WK5tm3F6/jxlVp5ufl9GRkZxr0lKytLqxw/XtNA87Rvr2mgVY4bJ5YtLdU8MTGaBppr4cIq+11SUqI98sgj2ozXXtM84eGaBtrboPXt29frGIKCPBp00Gw2m7Z7927tvPPO0wDtySef1MrKKrTQUI9x3dntHq2oSGz/X//6l+YArdJyYcaxUwNN+/VX79//vvvk387/aREREVp5ebnfc71y5Upt48aN1f4Wjz32mAbBXn8Le/aY8z/77DMN0EJDQzVACwgI0G6//XYN0G6++WZjudTUVP0cB2jjxz+hgV274YYbNNe8eZp142+BZrfbve73gwYN0tDnaaBVXnWV+H1CQrSKsjLNfd99mgaa++67q+y/69tvjW27vvvOe96773p9d8XevQ1+f6vrv6MZE06mf/V9/NnZ2Rqg5efnH1Eb1Fm8HDp0SNu6dWuN/8rLy7V3331Xi46O9lrX5XJpDodD++abb2r9fUVFRRqgzZ07t1bL5+fn1/rg60pFRYU2a9YsraKiok7r/fe/4u+ve3dN83jqfbeOK9ZzoN+TNHheu+WWW7SdO3fqN8jXNdC0Bx4w1/vxR/M+dOqpx7YP2dmaFhwstqWPQdoPP9R9O//4h1j3wQerzktJEfMWLNC02bPF+5QUTYuIiDBu2mvXrjWWHzxYLON7abdsKaZv2OA9/fPPxfRhw2q/vyNGmOfw3ns1LTTU3HZWVpbmcDg06KSLE//XmrwWzzxTfH7nHXObV16pabW5tEtLK7TgYJfXcbndmrZvn/dyr79u7murVq00QFu/fr2maZqWm5trnMdLLy019iExUdOysry3891332mAlpycLGY6nWJhObi1by8WXLfOPJgbb6z+AN5801juW9BsNpuWm5ur7dol/kZvuGGNhi5qNE3T3nrrLQ3Qhuk/Vv/+5td07ixOssfj0S6//HItwaoiQBvALxpo2vvve+/CFVfIRe7RevToceSTXg3Tpk3T/+YKNNC05GRznsfj0YYOHaoB2qOPPqp169ZNA4xr+OWXX/baVv/+/TVAczqdGqC98847mmYRFxpon1tEixDL6Ncd2kdyualTNS0wULzfu1fTrr5avH/++aoH8Ntv5vaXLPGe5/Fo2imniHlt2hz1OWpIjnZMOFmo7+Ovy/hd5+inVq1akZKSUuO/wMBAhg8fTl5enhFoB8LU7vF4RPpdLVm3bh0Abeoa0NCImDRJ1IjYurX65nBNETPJo4CoqCii9YA7l0vUi7CmS8vATRDxd0a5j6Ng5kwRbNu/v1n11JLNWWuk99LfpWVNl5bu/cGDKyg0o5RZaSmTKwN+rdWUi4vNjBJrACWIbBwQ10RtsrI0TQR+S155RVRTbd0aevWC7777jsrKSkC440pK/Lel8E2lvfFGERTrdIoY2OuuO/K+bNoEZWVOIiI0ZDynwyHK3Fu54w6xz1OmVBguEemeiI6Opot+0s46axV2u4jF/PhjqmTzzZgxA4DzzjtPFF9xu0U0vIybS00V0eO6awmAL77w321R00Q0sE774GA0TWPx4sV07iwyA8PCZgIwSq8Rc+655wKwfPlysrKyvFKj27cv5dxzzyU2NpYvvvjCyDSS9NHr2cyY4f07yzRp+Iv2vheHP5YtExfN1197TW6lu8sCAg4B3llGS5YsYfny5QQGBnLnnXdygZ6WJ6/hbtYDAT0rC9x64R3fNGkQbiPJ1VdfTWBgoH7dQbQM6omONv8gtm2rvkAd+A/Yldhs4rdq21ZcqAqFhQYL3e7evTtnn302N910EytWrGDx4sXceeedTJgwwbiBHThwgJSUFCOifvfu3Tz99NOsXr2affv28f3333P11Vdz2mmn0Ue2PW0EuN1utmzZUqUHSXVERZmVLU8m16iveIkySnCb6dISq3gpKwNdkx4VH30kXm+91QxePZrMppruqf7ES7du3jEJVvFiVlg158tMo4gI7+rkIO7tdrtoBlibmizp6WJZh0Nk38hL7/TTxT3ejAkrxukUg7bvdj0eU7xYB7krr4RZs8T7Tz4R2UI1sWKFuG0MGqQxZcpjfO0zoFrp0QNyc8WJDggIMKrEgpk5lJOzmJ9/Fn8bp5ziZsGCBUYQfnp6OrP1in433XSTWd3v738XP75Unlu3wpIl5hcXFJgHZWXlSq+LLzEwEIDff//dmPbnn3/yX+DFmTNh61YSExPp378/mqbx008/Ye1G4HJtYc6cOeTk5OBwODhdztRFxcO8DJSxcKF3YUIzA2lf7cTLjBlCCFx3nVf6khQvTqdQtjKTzePxcP/99wMiGDguLs4QLxJf8SIzmkBkPPXo0cPsVqmLDKt4OfXUU+lgUayGeAkPN3PKp0wxfxd/2Ub+UqWt9Ool/pBkJUGFQqdB884+/vhjUlJSGDt2LOPHj2fkyJFeZa1F1sJ2I0gnMDCQX375hbPOOouUlBTuu+8+LrnkEuPm1Vh466236NmzJ6+88orX9BUrVpDvrzUt0LOneDXakZwEWFOlo6KiCAgIICQkBF/x4vGYzfnkvU7ez1JTRX+Z2jav3LNHjD0OB1xyScOLl717zUzNhIQ9XsvIDBjwb3mR4sXf2BQcjNGZ+giZvoBpdenSBazx6WPHQn5+vlfAXECAOBm+cfGbNwtLUFiYyDq1cu654hrVtKpl8n1ZtkwE4SYm/sW///1vrrvuOuNp3R8yuDMhIcGrpL/sSbRjxw7OOEMIsRkzZjB27FguvPBCPB4PM2bMoLKykpEjR4rBVAoP2Z9Gmn42bzYtL3Ke7OhoRZbh14udtdCzG6R4KSgoYN26dVwABJSVCTMfpvXlhx9+8LK82GziB7/99tspKirieVmOVxdmXUkjFHGfeOABUW+trMz629TS8iKLtBUWiiqxemfPWN1M5XDcx++/m+LlrbfeYtmyZURERDBlyhRAVBuWywcEBHgJDxCWFmk9HThwIA6HwxQvelnxaH3Zzp07Ex8fT0d5EQORMo05PNw0LS5fLv644+LAX0aYvyJ1CkUtaFDxEhMTwyeffEJhYSH5+fm89957epaBICkpCU3TGK3bsNu1a8eiRYs4fPgwZWVl7Ny5kxdeeKFR1HmxIgtmvfrqq4bJ9MMPP2To0KH06tXL64lcIv/GTybxYlpeCg2ri7j5CdWSkSFu1tu2iWVDQ+GGG8QaUrxceaVoeuevDqGmidLsQ4eKBn1gWs1HjxYF3+TD2rG4jfyJF/lA//33wsUVFwdutyjsJQebjRs3UqYXmPFnefEtUOeL1XV0JKR46dkTLrwQTjtNHPu554oB1eVyGcLAbhcmF1/Li6wJM3Kk/9LxZ5whXn/5pfr92LQJvvxSfE9YmBhQCwsLjb8Jf1jFCy6XMJl99hld9ZO2w6L4ZDXZn3/+malTp/KOXhH15ptvFhdBRoYwNfXqZZ4QudP79glz1uuvi2nz55uNpUD8ILKs/mOPARBYWkogQojm5OSwbNkygj0ejJ/s229B04TLSt+vTp3MdM6yMlFhr3///gQHB5uVDZOS0HShkMRztGxZyY4dQjvJ68LhKAFyxPWUmQl9+4rjmTABXnrJLF7kdpuFopxOoS71vgnS8pKXt5sRI8S9KCMjg4ceeggwC96J73MYx5GcnIzTmruNyAYVtZrMtgrGH5YuXmL0a+wUXSBaxUuEFKZhYcJCdNpp4nX2bPHbyNotVuLjRa+hc88Vil6hqCWq4s9RIAs8paam8uuvv+LxeIx0xP3793PqqacyU39ik3TqJF5PTvFSYIgX8XqIyEgXHg989ZXpMho8WNzPQIiXP/4wXTL+PA8zZ8Jrr4lCdFLcSO+IXtH8qC0vJSXm/tdkeZFj36mnwoEDIv00JSWFVq1a4Xa7jZgsaXk5eNDcbkOJF7vdHJfbtMFw28iBp7Iy3dgXK0cqHe8rXjQNbrkFzjpLlJQvKYErroCyMhv9+2ficv1krPubVEZQxZ3qJV5+/lmM4HfdRVf9pFnFy1bLyXjooYf466+/iI6O5tJLL5V17UVXQPkQJC0v8gLq00dUzRs5Ulgnpk8X0wsLRVfMkhJxIZ57LrLwymkpKbjdbh577DH+/PNPvGq47twJW7cyePBgWrVqRUFBAdnZSxAxqnD4sLD2GIO4TCFOSMCWmAhAewq5/npxMTzwgOjuDOBwiOupffv2wl+3YYMIuvn8c9GCXN5Dtm0TQiYiQgQ7gWiLsG+f4YbTNI3DeoDV3XffTUFBAYMHD+b222/3+i2uueYa7HY7Y2VVQx9eeOEFHnzwQR544AExwcfyEqVp2ICRI0d6HzcQJn/38HBxw1u0SFQgPO+86oWJwyEsZvVRblrRrFDi5Sg4dOiQ8X769OnMmTOHbdu2ERkZyfnnn095eTnXXXcdy6WvBNPycuiQV7Vtg88+EwPFsQSyHm+s4kWam4V40Tj3XFGbYcoUU6AMGybcFQ6HGHjlTRzEPdvaA2rHDrjrLvPztGmiNcmyZeLB+6KLxPSjFS/SKhEc7L9PW4JP5OXIkaLEOghTvSy+KF1HUVFm2xbZq8dfdV0rUrzI487KEuOqP/e+VbyA6GMUGirqfMjGfXKgKi9P9TpGEEJEr3dWrXgZNUqM57t3C5G9aJHoWTR/vvjdTj9d/E5t2mjcffcaNm3aYKwra5y43W6GDRvGgAEDDFeSl3iRJX2zs+lmlwUGD5GXl4emaWzRe2kMtvSwmTRpknBHyj4CffuaOy1PiCxUKV1GMvJ4yhS46iqhujZuFCa0r77yquL67D//CcD//vc/PvzwQ7wjQYBvv8VutxsxIUuX/sJbb1Vy003rOHBA/P7GIC4tL23aiMqWQCIwaNBazjpLaJCff0Y/VyKwvX379mZAzK23Vg2Qky6jvn3h9tvFD1haCg88gNPpNCrzZmdns3fvXr788kvsdjtvv/22cP1YGD16NKmpqbz00ku+RwkI6/d//vMfMzZJWl7043MAg1NSuPCCC7DNmEF/i8UvRLYWr0vVRaBeKlYqmh1KvBwF2ZZW7bNmzeKJJ54A4NZbb2XWrFmGf/xneZdCmPili8PMNBDs2gXXXCMGiqb0AOLP8iJFzGmnrSUmRoiQDz8USw0bJizKsgneunViDJGx2N9+K14rKkQ8ZnGxCCwdMkTcq6VgGTHCdOscrdvI6jLyd+/0J1727xdPyrGxsQzQ/fc1ZRwdyfIiYxqlseHzz0U19FdeMUIaACE8ZH8sOVZLFi1aRGlpKR07duTMM8/Ul8/wOkYQQiY7W5xvef59iYgQvxEI64teoZ7WrYW3Z/lyca5mzqwkMrKMzZb0pz/++AOXy8WcOXNYsWIFa9euZc8eESPkV7wA4evWGVmEO3bsICMjg4KCAux2Oz/88APJyckEBgaajVml5cUavO9Tvt5oHHXNNcLnaLOJFKaffhJK9fvvzR9EFy+Dk5KYOHEimqaxb98+jFZFchDWA3/76x0at2zZwtVXawwfvpby8nLsdjvtpEK1WF7kRZQI7N69nblzhR6RrQg8ngXYbDYSw8JMZXn//aIjJQhTmWiBLT736yd+wNdeE69ffQULFxquo6ysLNbqy/bv3180sPRDYmIigXqg8hGRlpf4eKMv0fK5c2mdmorzllsY9s47JOuNwYJl3FNdxYtCcRQo8XIUyLTP+Ph4XC4Xa9aswel08o9//MPrCW2JNfsB0/qyxzvuk3vvNS0ulszyRo2m+REvc+bwztKlDAYqKrJ58EExV97TZFqzHF9AiJQ77xTvpUtoyhRhZYmJEcJHjzc0LBnSZQRHb3mpKd4FvNOnw8LEQ6+0vLRs2dKwvNSUcVRbt9GBA+JcyuKheXnenZb37xfznc6qfelkVePBgwcTFBSki8iDXscIpmbo3Lnm0ALpOvrf/+DHH8XY/8cfwoPRrZsQVmPGaGRmZlJcXExQUBAxMTEUFxezatUqpks3DbBLP4jqxAt//ukV9yJdRsnJybRu3ZpVq1axbds2EagL/i0vMTHeP6K0vDgc8H//J3yOAwYIU9WHHwolLJGmskOHePHFF43Yuj5yYL/pJnECVq2CtDRjP6R1KFM3bbVv354AGUTkx/KSAMydOxebTZzfX3+FBQvWAS8SHx9P4G+/iT+S7t3FDzRokBAAOTlCsEnxIttb9+4tLDQAd99NnB5bk5WVxUY9NqZ3797UC1K8REWZTwq5ucbvGJKdzYABAwgAnEdreVEojgIlXupIWVmZkR113333GdMnTpxIon6zksFsS5cuNQJ6wX/Q7ty53imUlgSWY8b69F7flJebosQQLx99RLuCAs4F8vLyuOMOc3zo0MEUBFbx8tBDIgBVjhFffSW6CIOwRCUmwtlne4851rZY1vtpXfAVLw8++CBdu3Zl0KBBnHXWWSxe/IshjIYPF8LBanmR4mXbtm3k6WYfq+VF047sNoqONr9/3TozoBbEmCuRBo6uXcUYbEXGi0gRIFpyVA3YlduQca7VIcWLHC8vukh87zXXCPeWTKb5S0/X7dGjB6NGjQJEduGPP/5obGunruKkeEmMi/MO8Fm82K946a6ruqioKNMd43KZ5iffsglS3CQmVlWKgwaJC+vwYdE10ooMIM3Kok2bNkbc2kA5+J56qnmxfvedIV527dpFRUWFIV6MfXS7zZPu4zZavHgxOZYc9Lw8cRPwchnJVKGAADM4bMECM8NKihcQvsUWLWDDBv6u+6EbRLxIk2ZUlPeTgn7dBZSU8PgjjzBFT8sGhNpXKBoYJV7qiEyFDgoK4uabbyYiIgKbzca9995rLNOrVy/Cw8MpKCgwntKgatBuRQXcc494L+turF5du6JlVioq4Lbb4PLLhUv8jjuE+T80VDx0Wp/ijwVNgyVLbBQXOy1WF4BiIiIijAMLQZynsDB4/HGxxFlnmUufc44QBA88IAbT1q3FOAEi0cLjgYkTTQuLzQZPPy3en3qqETsImPfTY3EblZaW8uKLL7Jz505Wr17N/PnzefLJJw3X0ciR4nhkca/Y2Fji4uLo0aMHmqbxre7vslpesrNFfIPNZoxhaJpGdna2V/Mx6Tr63/+8O2P7Ey++LiOoTrxUtbzUtA0rQ4Z4l96wjklWpHjp3bs3Y/SL94033sBjUcy+lpcObre4WHX3Azt20E8/yTt27DD+VqR48WL7drFuRIT3BQCmeDnlFP8+QJvNvzVAihc9hu2OO+5g6ZIldJSqvGtXUyl/+y2JiYlERETgdrvZtWtXVfFy6JD4I3E4xLb1H75zcDCVlZVebuRU3SyX1LataXKT4gXMG8LMmeLiDgjw/vFatjQyps7Qf+js7OyjFy+ffOJdJ0ciLS/R0d5/bJa0ui6xsTwiA9gCA6sqbIWiAVDipY7IrtWtWrUiMjKShQsXsmDBAvpaTNlOp9OoImx1HflaXj79VNyTW7cWAbsBAeIB0VKDqlbMmycKUX75pRgE33hDxCeUl4sn6GHDvIuPHi0//wyjRzv53//6ermMIiLCRWCgHswTjCnybr9d3BNfftncTlSUmGZNj5ZjRGWlEBSvveb93WedJbwGMi5GIu+nBQVi3doiH5Dj40WGi8fjISYmhv/9738AbN++nVNOEaEFF15oWl1iYmII0gffiRMnAsLiAN6Wl6lTxfs2bWDVqsUMGTKE6OhoWrVqRY8ePQwBI8fpL78Ur7KO17GJF+G6yMgw41h9t+HxeLyygoqKihg8eDC33HK9EdA7fLi3lcyKFC99+vQxSh3I7Z2qK9Fdu3ZRWlpKrm4WayPLDffpY+zIYP08+LO8eCFdRn36VBUod90lsoceftj/zlaHNAtaGiIO69gRW0GB+OGTk03VvXo1NpvN2LetW7dWFS8y3iUuTggYXZi11QOTrfWqpHg51ekUloyYGDPgCMzAGJki3bNnVVGgx11F6WIrNTXVsHbVSbysXClqFpx7rnnBgBBiR7C8AMK9JQs1KZeR4jihxEsdkYOyDJIbMGCAcfO2MkK/6/sTLzLmRSYT3HyzWeYd6h73IsVO377C0jF5sniQWrlS1Mo6fFgUM7vkElHgTMYG1hUZrrB+fauq8S6lpcajfggYrhSbTQyCR7qnWV1Bb78tHix96dOn6nRrUU55n83LM102ktJSIRQlVsuLfFrt06cPV111FSBM8M8+m0tWloiTlPEu0jUIpnhZsGAB6enp6HGL5Oaa4mXyZJg2bRorV640hO+uXbuMYFY5TkvhJeOE1q4146CqEy/FxcUc0HO5Zan9uLg4II2wsCJcLiGCNM387Xr2FK6usLAwoxYIwJw5c1i1ahUzZ87k9tuLGTpUhIxUxz5dqPbu3ZuePXsaxc8iIyON7e7atYsMPQYkJCSEUHnh9+plxKZ01ucf0fIig3Wt8S6Srl1FpHt1kcjV4WN5AcyLJClJWIiklSc/H4qKDNeRX/Ei412kyU6/ViJKSggAfvrpJyMDS4qXYVI4jR9vpG4bx2m9uK0uI0lMDABhuuBYtGgRmqbRqlUr/TqoJdLVl5cnnoQkZWXCXQfeGQc5OV6WF1tOjplCqcSL4jihxEsdkQNQa/nUVg01iZe9e8WAsmiR+Cy1j6x6Wte4FzlQjxolxMnzz4tA2EGDxHdceKF4oPrmGxH8OmqUORbUBXlvLiwMYs0a+fSrixdLLwCr5aW2dOggkkJmzvS2nh+JgADTxS7Fy3nnCVFgFTD/+Idw0chz7k+8SHefFCi7du2Q44NheWlnCWDp2LEjI0aMQNM0PvvsM0JDRRsWEOPQe++JGBEpfP773//SS1eo8gnZWmo+MFDEiMbECOGyYUPNmUbSLdOyZUti9B2V12V8vAjkXbjQDAh2OMQ4P3fuXMrKynjjjTeMIns//SRqtmiaRkjIKpYt844zslJaWspB/QT26dMHu91uuI4mTpxoWCH37dtnWGgSEhKwWQNv9Doh0Vu24HA4KC4uNkoQpFhPisRqeakv/FheDPEifYAREWYu/YEDXuJF7m8Vy4sM7oqNNawlPaKjycvLY7FuApXXRLIUdHrxOAOHwzun3Z940S0hIXoPJ6vVxVaX9GM91R4QPaEk8m/YbheiRFpeRHMrczmr5UXFuyiOE0q81BGr26gmhg0bhs1m8/KNy4e4oiIhUPbvF4OvLGYpxUtdLS/6uGoMnFbCwoRoWbhQuG4GDBADoixCWhekeAH4+Wd56eg1XixRyMGYlpe6MHGiCAytK1ZrttstasEUF4usDhDH+9134r20dvkTL9LU7q/yqz/LC8CVV14JmK6jSZPE7/DTT2apEbnuwIEDjYFZDjRWI8OoUWKMkKJhxQrxUFxUJH5HadmR+LqMwBQvERGi4u2iRd4Bv0FB5npFRUVGkUUpXgC/FaKtSDebjP0BeO6555g8eTLPPPMMCQkJhISE4Ha7jYq5XplGFsuLfc0aUixl6rsnJhLx7rvelXGhZsvL0eLP8iJ/c2sPAPmb799viJf169cbReE6yWA2X8uLzWa8/5vuEvpBr4UgLS/hUjj5sxpZG1DVYHmxV1ZilQx+XUb/+pe4OH3bOBw+7O2j/O47U5jIv+HISHEs8g/NUr/K2IZyGymOM0q81BE5KB9JvERHR9NTf1SWN/DgYPO+JotnDhkiAmvBaIfCqlV1C9o9UlaL3S4Gxn/+E047TQSNfPyxVucgV6t4+e03H8uLpXiNDNg9XlgzjjIyTBeMjPPZtct8uF6/XpxbKV7i4monXqTlpa2PQrz88stxOp2sWbOGbdu28eyzwggls3Y8Ho/h2mnbtq3h3pHiJTHRDJDVm/oa4mX5cjPg+Y47qpb0r0m82O3CN7hkiUg7B9NyYz2ub775hnXr1hkCG7x7NvnDaqmST/idO3fm+eefJyYmBpvNZtT+kD2D2sfFma6GXr2EGbJNG3C5GG+xYv4jPFxcqHowKuC/LUB9YMk2MpCWF6t4kb+5xfKyY8cOPB4PISEhpovG1/ICxh/8Gfp633//PWVlZWRkZBAEOKS7xZ8lV8a92O3+RVtIiBH8HGOZXEW8pKXBs8+Kjqb6vchg/nzxB9GzpzjOwkKzip41WBdM8WKtJonuNlLiRXGcUeKljtTW8gI1u44++US86lmmgLgvBwaKQdi3kF1NHEm8SDRN4513rgY2UlJiw6eDAbm5wq2kGxGqIO/NAAUF1YuX6txGRUVFhqujPtA0jWuuuYacHFGpNC/Pu5O1PO3WJIr168U9WcYlOp2HjbgM6dKpi+UlNjaWcePGAfCJ/qNaLfaHDh3C7XZjt9uJj4+vIl5sNpHB27q1WVhVipfPPhOxL+HhIjPLF3/iRQ6khYUraNVKxPrI/oRSvOy0xCt89913RiCpvKaPZHnZpFtQagoKleJFukn6BgcLVRkdLQZ3m80IUB0hs4+AblLJy7LMYKYKd+pUv4OjFAyFhaa1oSbxsn8/HTp00JuPCjp06GC6aHwtL5Z1+7dsSWhoKDt27OA63STXXh53YKD/jso9ewof8P/+550CJrFYQ2oUL9bu2r4Bb9JlNH68eQFK15E1WBf87yOogF3FCUGJlzpyNOJlsSXVR4oXeV+wipegIFF/Cmof9+LxmBZ2f24jK4cOHaK4uAiYBoisJI9HPHh9+qmIv3jySWFd9ue6slpeTAr9Wl78uY0uv/xyunTp4pU+fixs376dDz74gPR0MZjm5nqLly1bxH3VKl7S0swYkqgo2LVLWBGSkpJEujc1W17a+VGIf/vb3wBRZdYXuV58fDwBAQFVxAuI2JiDB03vhKyMLwN277lHhE/4UpPlJSvrkHFtyfG4Z08Rr2K4LMLDOXz4MP+nR+bKukV79uzxqkliZe/evUYtl141WEGkeCnSB7XuMoW6Vy9T3ekXbHtL1bxEOaDv3CncEeDdX6I+iYoyzVlZWSI4VcagWM6pVbzY7XavgGJrbx/DdWq1vOhuwpA9e3hL72j9md4cso+02LRuXX2K9+TJIqK/OnTXUQtjFZth8TWwpuhZRaGmmVaWceNErQUQVYhLS6u3vMjVZZEiJV4UJwAlXuqItCgcKWAXzJTRFStWGDVCrPc6h6NqKmpd416yssQgZ3GvV8vu3bv1dx8RHFzOzp0iQLR3bxFvcuiQEFCaJtwU1iJ3xcXiARUgOtoSrCctLz4xLxUVFUYwKIieN7J53+p6KiO8wYg6zhP/53mLFxDxL75p4jKhIj7evxWhm/7ULV0DmqZVa3kBDFeCVZBIfN1NUrykpqZ6nR/r2NW6tQhgBjG+WkoIeSHFSxdL2V15Xebl5XHKKS6v5Xv2NIN8W7RoIZodYgrNiRMn0rlzZ8D8jVauXMmbb77J999/z/vvv0///v3ZtWsXERERnD1mjCja89xzVfYt2SdAp6Mc3KyCR38AiLNk2cRa/aUyFkN3PRmF2+oLm83bdbRnj7AOhYWZShK8xAt4Z0MlyUC2TZtEcJHTaapPMAOMN2zgqquu4tFHHzVmdZPR4LW4l1SLLija6IHBnTt3JswaNHv4sHn+QPwxSL/qhg1CNYeGigDqoUNFkb+iIhG0Za2ua/kuiaabCJXbSHEiUOKljtTF8tKpUyeSk5Nxu90sWLAA8BYvsgq4FWvcS22QLqM2barGRPiye/dugoCWFNOjh/iC994T99zgYFEIbts2sU/Ll+PlVpJWl9BQjb59LTEC1cS8gLfraMeOHcZg/VddC9lUgyleRB0RX8sLiIBXGbAqA6Plw6a/YF0QA5LT6aSkpIT09HQKCgoMC4JvzAuY4uHAgQNG9WWJr3hp3bo1ERERaJpmpEv7Q8ZqPvBAlTEDgMOHDxvWEatQiI6OxqmLgV69DhvTAwJEHRqrtUZajMSyvWjXrp3REHHlypUcPHiQ0aNHc9ttt3HhhRdy7bXXkp+fz9ChQ3n55ZdJ2L9fuB3+858qQVpdfPoYxMl+YFbxopuTYixBpBHWzqTLlgn/nmxLXt/iBbyDdq2ZRlY16SNeelj6KRmWl3ffFa/nn28W6wFTvGzaBJWVPPnkk1yuWzh6ye+uS1qzL7oAaqffSKq4jGbPFmKlVy/heiooMGvHSJfR6aeLpxbpw5Tr+bqNfMWL7PehxIviBKDESx3xrfNyJM4++2wAo/OvVbxYXUYSea/ziYmrFplpdKR4FxDi5RfgLyAl7gMGDhSl92fMEOLk0UdFRpTsJfTgg2bZfWu/uZQUq0uhgJYhIV4ZG6H6jd/qOlon4xaoP/EihYc/8SItWFKAJSebQbTygb468RIQEGBkkOzYscOwzrTU4xZ8admypdHZ1zemx1e82Gw2v64jX158UYQqVFd3Ta7btm1brydtu91uXJvR0elGXZxu3YSAsYqXM88801h3vB4tbO2W/dxzz1FSUkLbtm0ZNGgQnTt35uGHH2bBggXiO6SiLSjwDnqlquUlQkZIW9Og9f0MKSri/vvv51//+hcBVnfj8uVCxZeViWWtcSj1hdXyIk10vunYlmwj8BYvSUlJQmDJ7qM33ui9bqdOwrJRWgq7d2O32/noo4+YPXs2F0s1XQ/iJVGPw6kiXqTL6NJLTfX+559CbMpmYnrMFiAsaSAUvvwtpNvIGvMSGoqmC1GbyjZSnACUeKkDpaWlhvXgaMSLpmlGiwDwL17kQ97Bg7XrTSQtL0eKdwERy9APCAPCM1awapWwDl97rfd96R//ECm82dkiLgbMcSo+XqsiXhKsT8uY4sVqeZHdbqEhLC954v88U7xMmCBepSHklFPMhA15XuPiNEOY+MZvWONeZCCu/C39UZ0g8ZelVBvx0rKlqM9jr+Yv1F+8i0QG7WZnm3EvvplGXbt2JTg4mFtuuYWQkBAmTZoEYFhefv/9dyNGY8aMGaxcuZJdu3bx7LPPGk0Ibdb+A9aKqy4XiS1bGpWIAQJkVo21yqBFOEydOpV///vf3iJo+XKR4w/C6lKX2iW1xdKc0fAn6t25DeRvl5UF5eVVxct33wn3TGKitxAA4RuW15Z+vQYEBHDeeecRLCs91oPbqLP+Osp6UykqMo/p4ovNHhx//CHO64oVwuJi7fk0cqTIYsrIMONj/FlekpPNQKzcXCVeFMcdJV7qgOwmHRgYaHSgPRKjR48mMDCQffv2sWPHDhITRTxDbKxRp8uLuDgxYFVWepefqI7aZhoB7N21C3lrKbYOPD4EBJgxgjLN1hQv0L59AeHh0k1QQGupEPSnPxl+2ZCWl4KCAqPKqz/Ly1lneQuyESOqZpsGB+dRWFhIQECAEecikaJg06ZNRoClHOD9Ud/i5UjUJF5k3MuhQ4e47TYxNspd913vxRdfpKioyBBv/fv3x2azcfjwYcrLyzn11FMZO3as/52wRnDLY/F4oG9f7L1701VX6hEREdjlQC0HQqiaqlxWZg6CTqdQozNmiM8N4TKy7sPmzWZWkzTRSWJizFbc6el06tSJ1q1bExISIixMspP2ddcJseKLJe7FC5meXg+Wl3FDhrBu3TpOl+nVINxCZWWiU3Xv3uYN548/4N//Fu9vvNG7K3dwsFkcTwagyz+kiAjz+Lp2RZNixmp5UUXqFMcJJV7qQLbut2/VqlWtK1iGhYVxmn7jnTt3Lg6HcOGvXWsW7rTidJr3MmtqcnXURbxkWlwa5VlZXk30fJExibIJsByn2rTRcDjgzDOleNlOjIzk1V0C8nlbWl40TfOyvKSmpnr11TkapMVEIMRLWppp6e7QwTsYesSIqpm25eX79N1OMawJEjm4f/jhh+Tk5BAfH1/9IG5ZviHFy+HDh5k8eTJvvvkmK3Tfl29sCZjiJTMzkzPOEGPkueeKeb7ixWazYbeYdyIiIrwCUp9++ulqr3W/lpddu8RFs2cPA/WLsn2bNsJtAv7Fi6wuKLOLHA4RPAogg8wbSrxIq4d0ofTrV1VM2GxecS9Op5PFixfz0ksviSJzsvLh9df7/47jIF6cBQVe/dUAYVYFs2370KHiySQjQ3SrdjpFNpMv0sIo/0blb2azmUKmSxfDimYrLhZxL6AsL4rjhhIvdUCWA4/1l7daA75xL/HxNbt5ZNZQbcRLTdV1rRQVFVFuMcmHVlYaliR/yPFr1y6RQepbf2vGjEpiYoYCa4mSNy4pXnRRJMXL/v37OXz4MA6HA5vNRllZmXEujxYz3gWk20jGW0ZHC2EoxUtUlGg8bLebqehi/8SA669eiRzcZYD2xIkTjUBYf/gTJJqm1at4eeKJJ5g6dSq33XYb8/UB80iWFys5OTmGAPeNSbEiXUdjx471dkP4YhUv8lhkGX+gh34hJ1tdrFbFHhNjuoIOHxZ+ShBmSRmfAeIHrGuX5Noi9026OK3tz634BO126NCBhIQE7FIgjBnjHdBmpTrxIn+fenAb4S+1XbrcpCUpJMQMBgNhjmvfvup6vq4vqwlTvu/aFaKi0KTwlSZPJV4UxwklXuqAHOxrG+8ikeJl4cKFlMonUGDz5s1069aNqbKLn05dxIuv5UXTNK8UXMmePXuwGnoiMK0C/mjXTliAZekLa8wLiBjEoqJ1AITJm7DliT0I020kXUY9evQQZeI5dtfRBq+BQFhepCFJ3o8vvli49K+4wowdscZi5uSIgi/+eun4ioKaXEbgX5BI1wtgHLd12f3791fJTtq3b5+RVm+loqKCTz/9FIC+ffvidDqJjIw0updbkTEvvuLFrOqbSHgNg8xDDz3ENddcY8S8VIfN6jaSlheLe7C/7jYaIIVSWJh380GHw7AckJVluo9iY71ruowc6d8dUx/4/i37xrtIfIJ2DWTGmEwT9IcUXnv3YuloWq+WFyOyXpKaKvbN4TBaMQCm68huB0tjTi+6djVz9cHbWjZkiLDejBwJdjsV0k0kb0RKvCiOE0q81AH51FpXy0uPHj1o27YtZWVlRrl0gK+++oodO3YwefJk/vOf/xjT5X3St72LL5WV5jJSvDzwwANEREQYqdkSX/ESiVk11h82m5kYsnWr1W0kXsvKyqjQA3UD5UyLeLG2CJAuo379+tFBvynWl3gRqap5XvOkeOnRQzzMT5tmzrNa1rOyNlq24U1CQoKRidOzZ8+qJnkfpCA5ePCgYa2R4jAuLs4reLVly5aiHxTW2jvCFda1a1cuk5VOLfz444/k5OTQpk0bVq9eTW5uLhkZGd5CevNmePxx2uhP477ipaY4GSspKSnMnDnTqPlSLb6WF4/HS7ycPmAAixcv5gEZQGUdBCXWuBdpeWnVynQbQcO5jMDb6hEc7D8QDapYXiQ2Wd9I1nvxR0yMub50d7rd5vEei3ipzvIiO5AOHOht7br0UiFcbr7ZuxCfFZvNdB2B9+/24YdCdOmC1CUr/8qgfSVeFMcJJV7qgLS81KZAnRWbzcZZujl6kbypgFel2YcffphXXnkFqNnycvCgcK2vXy+szm63eLhq00YUFnv55Zdxu9089NBDXnElu3fvriJearK8gHfci+k2EtuUwsRms+GQIig52TBxWFsESMtL//7960W8aJpmuI1EIUDvp06rJTw83PthX2oQux0OHBD75U+82Gw2I4h30qRJR4xxioqKMoSETJeurh9SdenS33zzDS6Xi/nz51dpr/DBBx8AcNVVV+FwOAgPD/dO2/Z4xMD09NP01v1nRyteaoPN5RIpspKyMqGkLW4je0EBI0aMIEQObDWJl+xsb7dR27Yi0BSqBtDWJ1bxN2qUGZjrS3XiRQaNV+cykvi6jg4fFjElNpt3BlZdkZYXX/EiXUbWztQgLCe5ud6K3h9W15HVbeRweGUdVfi2LVDiRXGcUOKlDhyt5QXEwA3egaZb9WjYM/Sb87333suWLVtqFC9PPy0SMG6+2btAnd2ucddddxmCZeXKlcyTaZL4Fy81WV7AtLysXWsGwkrLi3QJtYmIwCYHnaQkI+PI2iKgvi0vaWlp5Ofn43Q6GTZsGFCCzWZWk/XnxpcMHCg8EhMmeNi/X+yDP/EC8Oyzz3LTTTdx++2312q/fAVJdeLF37KA8Xt5PB4vC112drZRkv/qq6/2/+U//2wUB4rVXZPWZovgvyLv0RIsLwhZ/Q5EHwaruVAuI4WYvwh1f5YX+ff1/fcwZ45ohd5QWB9Eqot3Aa/mjAaaZhZnrKt4kb9NbKy3uq4rUrwUFgofr6Q68QLid6guB19y+umi55LdXtW1ZkGJF8WJQomXOiCfZOsa8wJmUKi0GLjdbrbrT8hvv/02w/UAxfXr1xvixddt5HbDl1+K9ytWwNdfi/ft2sFHH33E0qVLCQsLY4Je5OTpp582xMzu3buxPvceKeYFTMuLXtWfoCDzIUy6RrrLp/+oKDFTf3KVlpfc3Fwjpbm+xIs8hykpKYYVzOksMubXJF6CgkRj3aef3ofH4yEoKIh4a6qohXHjxvH2228bPY+OxNGIFylm8/PzWSYryYLRSgFELxyXy8WAAQOq7yekW+0AovQ4m8zMTMO1B/VreQmWMRbx8WbxuK++8l7IV7z4s7xIoeIb8wLC7yeLpjUUkZGmqPINVLXix/ISUFiITcYn1eQ2gqriRVrFjsVlBN5WEXm+q4t3qQtRUaJ+zSef+C/xrKPEi+JEocRLHTgWy4sULzIgc+/evVRUVBASEkKHDh2M+IK0tDQj5sXX8vLrr941vF57Tby2aeNisp7y+Oijj/LSSy8RFBTE4sWLDTdVXWNewBQv8oE4IcFMDpFujSTZYVeaZCy1XvLy8livuxGSkpJo0aJFFfHy8ccf07ZtW9bIgjK1QMa79OnTx4gdsdnyjPk1iRfJXj1WISkpyStV+Fioi3gZrT8Rz5o1i8LCQn777TcqZc8ZvMXL+3pb6GqtLhs3mum6QHhxMS1atMDtdrNZ741gFcu+NW2OhiDppmjTxrS86NYhAylaahIv1cW8HC9sNtG++/33zUp+/pC/YUaGeIrAEqgeH29c99ViFS8ej2l5OZZMIxACRZ5XKSiri3epK2efLaLda8DlK1aUeFEcJ5R4qQNSvNQ15gUgJibGyDjZtGmTEe+SkpKC3W6nvT7ipqamGpYX2XRRoiebGFmkMqnI40nl4MGDtGvXjn/+858kJCRwww03ADBlyhRcLhf79u2rc8xLcrK3RdvaLFeKl0S9IZxxE9YtLzJgV2bIDNSzMXzFy7PPPsuBAwf44osvatwXK9aS/lK8aJoZ91IX8VKdy6ha3G7CDxyo0ssH6iZeRo0aRdeuXSkqKuKTTz4xUp9ls8T169eTk5PDypUrWbVqFU6nk7///e/+90nvCo3+FGw7dIgBuqtFisLNmzdTWlpKZGRk9WnS27ebg+oRMCwvbdqYgZ8yk05a42pjeanJbXS8OOccqE4YSlq3Fn8MHo8RqBwqz1VtrqGuXcXfRmGhCG6uj0wjiW/QrhQvNaW51xNelheb7cgiTqGoJ5R4qQMyYPdoLC/g7TqS8S6yIFg7PV0oLS2Nli2FuxnMhI6yMrNNyfPPi7g7idMpFhowYICR1fLggw8SGBjIokWLuPHGG3G73bSwpJtK8VJTobqAACOpAPAWL9Jt1EaqGzkIWSwvu3fvZrpeffSee+4BTPGSn5/PypUrDRG3w1pe/gj4s7x4PCJ4VAYvH4mjFS/2adMYe8cd2P0EPPqKF2nZ8idebDYbN+tZOG+99RY/690ir776arp3746maSxatMjIQps4caJ/0XzoEHz8sXh///3GNCleZHdoWdRu8ODB/i1NmZkipVd2hDwCXuLFN4ZGFtipi3jJzq7qNmpM2O1mJL0uSuskXgICzHTq5cvrz20EVYN2a4p3qWcqrJaW0NAjx9IoFPWEutJqSVlZmVF/42hiXsBbvMhBW/ZJsVpebDaqxL3MmSNKRLRtK9zYd91lbtfjEVYM6yDZvn17o06HzFRJtNxoIgCXy1VjoTrwyn72a3lpLf1I8pxYLC9FRUV4PB4uvvhiRuopqGFhYbTUsyv+T1oMwHBp+LJjxw4jWBWgvLycbXpgau/evUVHa6CyUjy1JybWLv7xaMWLTY9Lsb/yiuE+kEiLRnZ2Nnl5eTVaXgCuueYaAgMDWbt2Lbt378bpdDJ69GjG6ALizTff5FtdsU72VwkVRP+Z8nIhPC68UEzLzKxieZHiZYhV9VrZskUEfG7dWqXJoj+C/FleJPKJX4oXf60BJI3B8lJbfOJeQqUAqe01JGvXLFtWv5YXa62XAwdEVWK7vfq073qkwuqWUi4jxXFEiZdaIgd5p9NpDJh1pbaWF6iaLq2312HCBHFfuuwysyWJ2y2CPn0HyWuvvdZr0IuzpIFG609ItQ3ate4TmOIlVrpPfMSL/Can0+lVwwZM64vVVbRr1y6vmA+A4uJiRo0axXnnncfKlSsB2LZtG5WVlURHR9O2bVvLbyEG09q4jOAY3Ea6mrSlpVWJ8YiIiDCCf2+88UajAF2iDGLyITY21nATAYwYMYKIiAhDvMybNw9N07jwwgvpWV08hhwEk5PNgTA7mwF6Tvj69etxu91HFi+yQiqIGJoj4GV5SUw0U4xbtDAbEdYm5sUasNtUxIv+N2pYXo4UrCvxJ16ONeYFvN1Gss5O9+7HFu9SS1xWt5ESL4rjiBIvteSwXtMiMjKy1n2NfKlJvEjLy+HDhykpKfESLyUlMHu2+CzDHoKCRADv999DcbEYmPw94T/77LOcf/75AMRZ6oLI21ptg3bBv+UlRloffNxG0vN92223VclukeLF7XZjs9lwOp1UVFSQah1Agddee42Dut9MBh5bXUY2m42AgAC9mFwe0PDixWZNAfPjOjpTr9D6tZ4K1rJlS0JqiAO45ZZbjPeyFtBoH3P/Q9VVQgXvEvNy0Nc0klu0IDw8nLKyMlavXm1kNdVKvPiWsfeDl3ix203XUb9+5mBal1TpgwfNVN/GKl5k7QBdCNbZ8iIL723YYKZY17fbSP52RyiqWF94uY2UeFEcR5R4qSX9+vWjoKCAl19++ai30b17dxwOBzk5ORQXF+N0Og1XQ1RUlJGSm5aW5uU2+v13EfPSvj3o5WIAkUl6/vk1B4Y6HA6+/PJLZs+eTUdLMaxgjwcHVS0vGzdu9BIRRxIvkTKi2Mfy0qVdO9q1a8fjjz9eZZ/aWxTG8OHDDXFjjXvJzc3lhRdeMD4vXbrU2D/w7kck4l5m0759GdXFtFopKSkxaqAk1fapGURJY2sK2Pz5Zll8nRkzZrBgwQJuv/12unTp4iVO/HHqqacyYMAAHA4HF+pun9jYWOP4Ro8erdeyqQareHE6jYJn9qwso7bQ9OnT8Xg8JCYmerUp8MKaul4L8RJkTZUGU7z07WtaWOoS8yIJDTUDfhsbsljeL79AZWXdxUvbtsJ8WVlpdjytz4Dd3Fzzt7P2wWhAKpTlRXGCUOKlDgQHBxsBoke7vrVAWJcuXby6GVvjXqzp0jIL9swzzVRlSXXN/6wEBQVx3nnn4ZBt63Ui8La8pKamMmjQIPr06WMICWvbH39uo3CZYSLN37qV4dF772Xbtm1+g5s7WPqmXHzxxUbqrjXuZerUqeTl5RGpP60vXboUTdO8LC8S8Zss4733FnPeeX5PgRey7kxERAQx8qm1NmRmYqusxGO345Hl0994w2sRh8PBmDFjmDZtGjt27OCZZ56pcZM2m4158+axceNGrxoud955JwkJCVVcblXwbe4nB0NL0O4nn3wC1GB1gbpZXiorCfKtWnjjjUJZT5pk1h6pjXgJCjKypIDGa3UB4fYJD4fsbGzz5uFwuURjwtq0dAezs7OV+nAbWS0vssLxcRIvym2kOFEo8XKcsVoMulvNGnjHvVjdRlbx4kt+fj7FxcVA9bEVloW9PvqmS8+ePZuKigry8/O56KKLKCgoICwMrrpK3Let5UFktlGo/t2+lhd7RYV3+XoLvuLF1/Jy8OBBXn31VUAErTocDjIyMti/f38Nlhezou+RkOKlY8eOdXMB6kKvLCYGzx13iGkzZ5o560dJy5Ytq1wLN998MwcOHPDbeNELX/EiXy1BuzL2ptbiZfPmKsHIaBr88YcIvs3Oxu7xoNlsplg65xxYs0ZUw5XipaxMBBPXJF7AW7A0ZvESEGBk8Nj1LDratRPTa4uvFa0+xUt6utla/Ti5jdzBwWjy+JV4URxHlHg5zlifrn0HLH+1XjZsEPGTNhuMHVt1e1J8HCm2AvDuaIsQL7KIGcAPP/xgvN+6dSuTJk3C4/Hw4YeiKq1M3y4vL2fVqlXYgSBpzfGJealpQB80aBDBwcGMGTOGzp07VxEv06dPp6SkhKFDhzJhwgSjKeIPP/xAuu62sZ5HX/Hy9ddf88QTT+CylEv/4IMPGDVqFHv37j36YF39XJe1bIl25pnCXJ+fL+p2nCh8s1YslpeBPp2OqxUvmuYtXsrKQO/PZDBjhmiQeM01ZpfOVq38p3ZFRlqrGR5ZvFhdR8ezQN3RoD9B2ObMAUCri9sRvC0vUVHV91KqC9JttHy5qEPTsmXt6gXUB9beTEq8KI4jSrwcZ6wWA5kmLbFaXqQRRWatDhjg/6H0SC4jg8pKkFYS3RXTwuFg/fr1bNq0ieLiYqOq64wZMwgKCuL777836rRYWbZsGUVFRfRv3x6bzDaSNzB5M5buJD+0b9+evXv38v333wNmxVcpXqSIuvHGG7HZbEbrhLfffhsQosNasl+KF+nKuuWWW3jqqad4TS9BnJqayq233srvv//O5MmTj1m8lLZs6R2keiLFS3WWl0OH6NatmyFobTYbgwYN8r+Nw4fF72WzmU/sVtdRZSU895x4/9132BcvFu+raauA3W66gg4fBilwayNeGrPlBUzxIjPj6ipeBg0ya6HUh9UFTMuLPM99+lT1LzckUjwp8aI4jijxcpypq9tI4s9lBKZ4OaLLSPZgASPl8wz9Sfz999/n119/pby8nKSkJK655hojw8Vapl4iM3+ukX1nWrQwTedSvBzBlRIfH0+4frOTlpfU1FRSU1ONtN7x48cDGAGrsjt1Hx9/vkyXzsvLIycnx8gMe/zxx0lNTeW+++6jVBdTX331lSGOjlq8yAFWVvDztVIcLyoqzJLwftxGTqfTsFp1797diB+qgrS6xMfD4MHivVW8fPedeYyahv3558Xbmp7upevIms1W3fc3JfGSkgKWv7U6W17CwkRNHqifYF0wxYvkOLmMJJp8cAkLO67fq2jeKPFynOnUqRPJycm0adOmRrdRRIT3g8yRxMsRLS/SdB8UZAwW5+hN2z788EO+++47AM4991xsNhuD9UHM6lYCEY8iRcTFsgiW9QlSuo1qsLz4EhsbS3R0NJqm8d///hdN0+jfv7+RGSMtLxKrAARvt9Eui5AoKSnh3HPP5auvvsLhcHDqqacCZmDwsbiNANPycqLEi6yL4nCYT78WtxGYbRm8XEaZmaKS7ocfis8y06h9ezPQU9Z60TSQWV/69WKTZZ+rs7yAKV6kMAoKEv/80ZTEi83m9ceoWeK3ao2Me6kv8eLbOPE4BetW+X5leVEcR5R4Oc7Y7XbWr1/P9u3bCfbxd1stL5qmGQ94ISHVN4c9oNcdOaJ4kfEukZGGSb9/ly60atWKzMxMo/nfueeeC2AURdu2bZtX7Mjnn3+Ox+NhyJAhtJWDkXXwqaXlxYrNZjNcR++8847XfoAQfNasJV/Li1W87N69GxAp0E6n06hvcscddzBz5kyclhiNY3IbgWl5OVFuI+kyatWqqitCnzd58mSuvfZaHn30UXO9uXNFCflnnxWfpcCwihdpefnzTxFLERQkWppb+kVotREvUhjVVNjRKlgae8wLeD9J1PUaAtHsMCxMND6sD3wtL8dZvGgy/qweGn4qFLVFiZcTQGhoqFfMhkQKkJKSEnJycgzX0WmnVf/QWmvLi1W86OZ7Z3ExV155JQCVlZWEhoYa1V3bt29PeHg4LpfLy5rxsd5H58orrzQDcqwDzlFYXsB0Hcm4Fat4sca9QM3iRe7r6aefzv16r59WrVrx5JNP0qlTJ6OfENSxxgs0PreRb7yL9b0eyNu+fXtmzJhhdC33Wm/bNpFeaxUv0qq1b5/4fZ96Sny+5hoRBCqzrKDmoFApVmojXpqS5QXgjDNEphWgdepU9/XHjBF/jzfeWD/7Expqum3t9pq7YzcAnsceE+0lLrvsuH6vonmjxEsjIjg42Gi+l5aWhozn1Qvk+qXO4iUqyow9KCjg2muvNRYZO3asYQ2y2+1GQLF0HW3evJl169bhcDi47LLL/IuXo7C8AF5VeGNjYw23lUSKl+Dg4Cpdkf1ZXjp37swTTzzBM888w/fff28s8+ijj5KQkMCIESOMmJta4fEYrQFKfd1G+/fXWazVC/7641jdRn46XxvzJMuXe4uXmBizDH7//qIgm8MB990npl13HZoe21BvlpemJl5at6bynXdYf+utR5/VU58NDG020/rSrVv9ZDDVBadTVLM8nkHCimaPEi+NDGvcy1NPwTffwG23Vb/8UVlepNWnoIC+ffsalVgvuOACr1Wk60i6XmbrPQr69+8v3Dj1aHnpZjE5n3POOTgsHbBBlM632WyMHDmyyjx/lpfk5GSC/7+9Mw+Pqrz3+HcymSQTQkJCEpKYEFZBNllEClbWEFCqoPd6tdYHuVW5KmhRi4oiCJX2Sl2uWJU+VI1eq1a8BaNoFDBgKUsbJOwGCEvELFSWLGSbZN77xzvvOe85ObPmTJJJfp/nyTPbmfec952TOd/5rVFReOqppzQValNTU3HixAn87W9/8+v4cO4c4CpK1iB8/AkJ6kX65En/xjMDT5aXujo1+8Td+wCeAy+LF0B1O/zwAxcTGzeqzRfj4tD88ssov+YaMFc7A0P0MS+dSbwAYHPn4rRZbh8zEOKlreNdCKKdIPHSwZDjXhISgFtucf8jraamRqlt4nPAruQ2EhlIH374IdasWaOxwgBqLRVhedm6dSsA3ioBgDbmQmCC5UV2GQnGjBmDvXv3KtViZeRUadny4g673Y4wf3/5imJ+KSlgIm7GYmnfuBcj8RITo5bXl0WK0fsAYOfOluJFFBSaMYPHvujKFrN587Bn6VLPAZpCvIh162TipcMhBHUbZxoRRHthUGGKaE9ky4s3RLBubGysYQyNBoOYF/HclVde2aJ5IqBaXg4fPoz6+nrs2LEDAJT0W8XyIl88AxQvAwcORFxcHJqbm5UGhXpGyY2dJIR4KS8vR6Or15In8eKVykp+YZYtPK6LMNOnpA8YABQU+B73UlzM3Tk611dAGIkX8fj0af660TqIzw3gHY5F/R8hXh55hKvmPn0CdwUIsSIq9XoSL6mp/Jy02dR6QYR/TJ7Mz0NRvoAgOjkkXjoYsuXFHYwxWCwW311GgEfx4g4hXo4dO4bt27ejvr4eKSkp6v5MdBtFR0fjm2++AQDE61M/3cEYcNdduMIlmIRw6dmzZ+A9qAoKeBXU++4D1q5VnxcWBL148SdduroauPZaLgjOnm19bII38SJiYty9D1CFi92uCgeLJbAsGhn9+rur8SL2vWcPF4tGFXsJ76xaBTzzTNvHuxBEO0Fuow6GEC+iCqyeHTt2oEePHlizZo1iefFaoA7QBuxKMS+euOKKKxRryFrXhXzy5MlqPyATA3YBnkWkzyTyyNmzwPvvI/qttyB3l9EH9PrFhg08OHfdOi4A5H0BYHqh6E/GUV4ez+45fx4QtVJagxAnevGiq/WigTH1eXmdevc2N+BSL148WV4AXvxNalpKBAAJF6ILQeIlACx79nAz7d69rRuorAy44w7e8M6FKMC2e/dupRy+zKZNm1BVVYXFixcrMSgay8upU8CLL/LqqzIeYl7cYbFYFOuLKOU/depU/qLTyS/CgCmWl4AQRdoApEhf3K1yGe3axW+dTuDVV9Xn3Vle/Il52bhRvS+7bgJFiBB9sTNdrRcN1dW8WSIAzJ6tPi9cRmahFyvexAtBEIQfkHgJAMsHHwDbtwPvvde6gd59F/jLX9S+MeBl3J955hkAwAMPPICN8gUPqjupsbER7777LgCdeLnnHuDXv+aN9GQCcBsBquvI6XQC4JYXALwsvejvIgdZtsLy4jeSeMmQYn4Ctrw0NQGu1gQAuPVFiD5PMS8AL4Pvac6NjcCmTerj1ooX2YLizvJi5DYS7+nWTdvpM5BKsZ7w1/JCEAThB0ETL6tWrcKECRMQHR3tc/wBYwzLli1Damoq7HY7srKycLw9m965weLK8HGbzVFeDmze7L7OhsCVgowjRzRPr1ixAvfeey+cTid+/vOf46Ao1Q7jWBhFvJSX88qpgPYiDAQsXuTuzf369VMLu4m5x8Wp7aaBdrO8pEl9VQK2vBw+zGNAunfndSuqq4E33+SviXXXu42Skvh6MsatXu7Yvl0VQoD7c8dXZAuKviqtJ8uLHGQtpZCbbnkh8UIQRBAJmnhpbGzEbbfdhgc8FSnRsXr1aqxZswZr167Fnj170K1bN8yYMQP1bfEr3h/ERchdQOQvfgFkZ/OMDSF0jBB9g86c0dTksFgseOONNzBp0iTU19djw4YNymsiC0muyaKIlw0bVMH07bfafRnFvFRXexVYQ6VqndOkX+oWIRz0v/ply4t+7KYmjeBoNdJYqUI0oRWWF+EyGjcOePRRfv+VV/h+3FlefE2X1lnQWm15EcJETo0WeBIvsrUmPp6LNIDEC0EQIUXQxMuKFSvwyCOPtGii5w7GGP7nf/4HS5cuxezZszFixAi8++67KC0tbeE6aXdErIjRxaGhgfeDAXg33muuUZvcyTQ3A0ePqo+/+07zcnh4OKa7eqiI4F2n06kE6b700ksYO3Ys7Ha7mkK8fr06wKFD6i9zwNjy4nBotzFAFi9KvAtgHKwLqOLF6VTTZAW3387TYl21WFqNiLkB0EvqnxCw5UWIl/Hjgbvu4nMrKeG3IoZI3+4b8B6063TycwFQ+7+YJV704hHwzW0k3vfss7yOi66WS6uhmBeCIIJIh8lLPHXqFMrLy5GVlaU8FxcXh3HjxmHXrl244447DN/X0NCABukCXOW6SDscDk1DQTNQxnNZU1hFBZp0+7D84x8Ib2wEi48HYmNhKS4Gu+UWNB05os3mKC6GTbIoNR04AKYrMCVqvhQXF8PhcKCsrAwOhwNhYWFIS0vD1q1bUVdXh/j4eDh++AHh27fDAoDZ7bDU1cFRWAiMHg0ACK+shAWAw24HIiOV7BzH+fPGF0AXCQkJGD58OEpLSzFp0iRlDZyuC6OzZ080y2sQHq6OXVWlCqXGRoRv2gRLUxOa9u4FM+GXfti5cxCVWBJdvV1iYmL4egTw2Yfv3AkLgKaxY8GsVljeeQfWX/8aFpdbjw0cCIeruJ08fljfvrACaD52DE6D/VoKChD+ww9gMTFw3nYbrM89B2dFhXbd/MTyww8IB+BMSmo5Tnw8bDA+P8PKymAF4ExM5O+75Rb+xyfl077F3L2tcXh0NCy1tXxbu93n8UMBX9egM0NrQGtg9vz9GafDiJdyV+poL13mRK9evZTXjPjd736HFStWtHj+q6++QrTenG4SdRUV6AYAP/6Izz/9VFPMrP/GjRgGoHzgQOxbuBDZ992H8OJi/P2111ApNXFL+cc/ME4a89SmTTiiK9Al5n3kyBF8/vnnOHbsGABeB+Wrr77SbJv55ZcY6XTi4oABcHTrhuT9+3H43XdxxjXGrIsXEQ5g27fforasDDfa7bDV1WH7p5/ispf+LE8++SQcDgf+IcXRFO/ejasAlNTXY//nn6sbMwaRw7Lls8/Q6HIf9Dh2DJNcIvPQtm04467TpB+MOXgQIgIlzCVak5KS8MUXX/g9VkRVFW5wWU6+qqyEQ8zpt79F5IULSPjuO1RlZuLy5s0AgM2uWwDIqKvDaAAXdu7ETnktXAz+858xCEDpiBE4d/EiRgE4d+QI9hhs6yuZ+fkYCaCCMfxDN46tqgo3ArBcuIC8Tz6B06Ymkg/75z/RH8CJqiocbcX+Ae0aGJEdFQW7S7xsLShAQ3u0UAgy3tagK0BrQGtg1vxrXd8XvuCXeHnyySfx/PPPe9zm6NGjGDx4sD/DtoolS5bgURGfAG55ycjIQHZ2NmI9FcYKAIfDgc2bNyPa5Q6xOJ24cdw4jeXCmpMDAEieMwfT77gDYR9/DOTm4vqLF+G88UZluzCXK4lZrbA0N6N/QwP6SK8DwNixY/H444/jwoULmDp1qhL7M3DgQNyo29bqSuuN/c//5O6U/fsxvLkZQ2+8EWhuRrjrvZNnzwYSExEeHw/U1WHS6NG8AZ+fazDAJUoyRo3CFbpjYVFRsNTXI+u665QsljDJpTI8NZUfVyuxSqnMfV29XUaOHNlibXzB4rqQsyuvxPTbb3e7nZj/9OnTYXOJAktSErBmDRJ//NFw39a33gIApPzHf6BXejrw2mtItlgCOk5B2L59AIBew4e3HIcxsPnzYamvx8zhwwFJNFs/+AAA0P8nP0HfAPdvtAZGhPfqxevaAJh2660tY3NCGF/XoDNDa0BrYPb8q3xIIhH4JV4ee+yxFv1v9PQLpEU8gBRXh9qKigqkSpaAiooKtZeOAZGRkYg0+BVvs9mCczIxpskasV28qNb+YIyXWwdg/elPYbXZgDlzgNxcWDdtgnXlSnUcV4yLZepUYPNmhH33HcJ0x5uamoqYmBjU1NSgtLQUZWVlALg7STO3H39Usoyst98O/POf/H5hIT8GUUUVgC0hgZdhj40FSkthq6vjj/3E6gqWtaam8n3IREUB9fWwNTerY0tWG+vFiy3fEwhSzMvA5GR0794dt9xyS2Cfu2vNLBMm+PR+zfnliuuylJXBVlOj9pkRuD43a58+SoxQ2L/+1eLz9gvX3MNSUozHSU8HTpyArbxcjbMBlCBnw8/NT7z+j4mg3fBw2GJjO2XX4aB9z4QQtAa0BmbN358x/BIvSUlJSNIHaJpE3759kZKSgq1btypipaqqCnv27PErYynYhDU2wiIHolZUACKd+MwZnq4cHs4DdQFg1iz+pf3ttzxjRWQGiUyjf/93nlZ98iRPL5ayZiwWC/r164cDBw7g5MmTSpq0qMKrsHMnDwAeOpT3shH1V/bv50GzQmxFRvI/wK90aUNEpo/R+WC387ggOV16586W720t0jiZcXG4dOmS/w0XBXKwrr907w5kZPB06iNHgOuu077uCrLGFVeoF3R/AnZff51/bvfcoz7nKWAX4Mdz4oRaXM/X95mJmGtcXKcULgRBtB9ByzYqKSlBYWEhSkpK0NzcjMLCQhQWFqJGSgkePHiwkgZssViwaNEiPPfcc8jNzcXBgwcxd+5cpKWlYc6cOcE6TL+x6X1ycsaRuACOGqWKkORk9YL42Wf8trlZzS6aOpW3s2cMKCrSjr13L16orEQK4Fm8iAuUyHoZMICn0NbX8/3ImUYCd1V2L1zwSdBYhNXDqJGevlDd999rL6JmVJcFNJYXtEa4NDby3jpAYOIFAIYM4be6mj1oalJbAaSnq2Lv8uWWtXDOn+dp2nIM16FDwIIFwL33arPWvIkQIZL1dYHaS7wQBEGYSNDEy7JlyzBq1CgsX74cNTU1GDVqFEaNGoWCggJlm6KiIlRKLpjHH38cDz30EObPn4+xY8eipqYGeXl5iOpAPTtaiBc5HVVYF/QXQFGTxVViH6dO8Qt7VBRvgOfuwvfii5h+5gxuAxcvosZLb32mjvzLHgDCwtQ4lm+/1dZ4ERj1N6qs5P1lJk7k6b2eEPVrjJoois9LXJyFqBOYYXmprdVe/OUCcP6yYwcXE716cetVILj7DMvL+VqGh3PBILonAy1F3P/9H3evrVqlxIpo0t9Xr1bvu1xRHi0vgFY0Op3GncCDBYkXgiCCRNDES05ODhhjLf6U8vLgtV3kGBqLxYKVK1eivLwc9fX12LJlC6688spgHWJAhHsSL+IiPWGCdhshXrZu5cXohMvoqqt4ppK7C5/rQtMdXiwvQrzI1V9dKdLYt8+z5UUWL0VF/KK5f7/3vk0XL/JbI/EirE7C8iJE3Zgx/NYM8aIfw1MxQG+I7KSZM7nwCwR3n6H4bFJT+dgWi2p90YsXV68qOBzARx/x+7J4+eAD7pr86CP+WVmt2ngWGXEuyOLlwgVVlMotHYKFEC0mB84TBEFQbyM/ces2unwZKCzk9/WWl8GDuSunsZH/uhbiRVzw3F34XL++o8GtVCJg1614kau/GllevIkX8WseUIuqGWBpboZFuJs8WV6EeBGiTog4M8SL7DICWmd5EeLlhhsCH8ObeJE/GyFeZJej06mKF4D3vTpyhBcytNl4Kf/mZuDJJwERA/bUUy0bRQrEOSK7jYRYio8PKEjbb8jyQhBEkCDx4ifhUuYOANXyUlDALy5XXKFeOAQWC+8eDQD338+bMQKqi8KLeLGDp6AzxhAREdEyaNqo47GwvOzapfbnkcWL3CJAINfT8VDV2CavgVHfKrm/UV2d2qpAiJeamtY3bhQCSPRVCtTy8v33XEyGhQGuisYBIcrsnz2rFYRG4kW4bGTLy4EDXJDZ7fxYdu1SG3ZmZwMiU+3DD/l5MWoUsHSp++Mxsry0ZbwLAFx/PU+PlhtAEgRBmACJFz+xiTgL4V4QFwThZrn2WuPMimee4SXY6+v5hQpoKV5OnNCW65csL4L09PSWgalGF8ihQ7lYcDh4NhPgn+Xl8GG35e5tIug6Jsb4F7xsedm7lwetpqQAV1/NYz+AlpYTfxHipW9ffltV5T1OxwhhdfnJT3jgdKDEx3PXEKBt+2Dk0jNyGwmry5QpqogSXctvuw3IylKtaRER3DIjN8TUIwT0uXPqOdXW4mXCBG4Re/jhttkfQRBdBhIvfqJYXlzF1xTLi3AFuevlFBEBfPwxIBcGE+IlLY2LieZmwFVFF01NirDoKaVPt3AZVVWp1hNZvISF8UaNb7yhWllctXQAeBcvgFvXkSJejFxGgNbyIqwu48ZxUSdiLVrrOhLiR2RYOZ2a5pY+Y4bLSGBkQTOyihmJly1b+G1WFjB3rvq8zQbMns3XbvVqnt316qtqer47EhJUESkEVFuLF0AVqwRBECZC4sVPlJiXgQP57blzPM1ZiBdP2SqRkTzm5d57ec0OUdBP7kzsyiiS3SCJ3bop991mGsXGqiJFEBbG3VSHDwMvvgg89pj6mpF4EW4jMQc3riPFbeROvMiWF1dTSWW9zBIv4v3p6ar1x9+4l8ZGVTQES7x4inkR4qWxEfjmG35/2jRe2DAmhj+ePl11zWVl8XnPn+/9WCyWlnEvQrwEqVYTQRBEW0HixU+UbCORBVVfzwWAuGB5S7WNigLWrQP+9Cete0l/QROpsgDipVRxn4J19WRkAI8+qrW8iPoscraUsLz813/x2507DTtnR3izvMip0kK89OnDb80WL4mJ6sXd37iXHTu4tSY52a8WCW7xV7yItd29m6d+Jydzi0p0tFqQ7r77Aj8efdxLW6ZJEwRBBBESL36iWF5SUwFhEdm7l7tuwsNVC4O/6C9okniJk+JKfEqT9gUhJs6c4ZYjQBUv48bxgF+nUy2sJ+Gz26i+Hjh9mt8XsSlCvLS2UJ1wGyUmqtksvlpeGOMxIyKIujUp0jJ68cKYbwG7It5l6lT1OF54gVddbk2BRneWFxIvBEGEOCRe/ESxvMTGqheB/Hx+e+WVnoMoPaG/oEniJUbqWh2Q5cUIEbNTVcUtFk6naoVJTVVjc4Q7Q8KreJHdRkK8BMvy0rOnf5YXp5PHkNx9N1/rwYOB5ctbdywCIV5On+ap85cucYsK4NltJMe7CMLDVcEXKHrLC4kXgiA6CSRe/ESxvMTF8YqsAPD11/w20OqsgEe3kV3azG1rAH/Fi92uHv+pU3x/Dgd/3KsXMHYsvy8CbiW8xrwIy0tZmWoNaQu3kS+Wl8JC4NNPucj87//mBfkCbCbagsRE9XM8elQVlgkJmp5Vms/64kWlmadGvJiBvsouiReCIDoJJF78RGN5ERd/0TE5SOIlSkoBNs3yAqiC4vRpNVi3Z09+YRd1Yo4cadGDx+eYF5EynJzM4zgA87ONZLeRL5YX0R9owgTgiScCt5S5Q8TO7Njh/rMRn3V1NXfLOZ3caiOsYWYh9zeqq1PdR+K8JQiCCFFIvPiJxvIifsGKLtNBEi/hjY1YvHgxlixZgni9YAg05gVQ3RKnT6vxLiKo94or+Pyam9W6NC58jnkRzSeFSALUeQbDbeSL5UXMxV1Ke2vJzua3eXnuxUuPHmoK8bvv8lszsp30yJaXDRu4C6tPHzXYnCAIIkQh8eIn4UZuI4EZ4sUgYBe1tVi9ejV++9vftnyfWZYXIV5EoTWLRbW+6FxHPqdKC4uNHLthhuVFbsroyfLS1MRrosjiS1heRowIfP+emDmT327fDhw/zu/rPxu5v5EI1g2GeBGC9tw5Xu8HAObNMyc4mSAIoh2hbzE/sRkF7AK81oio1RIIHgJ29W4bBYdDDbI1y20kxAvgXrz46jbS7wdoKV5OnuRdlP1JcxYuI5uN17ZxZ3l5/XVe3fWXv1SfE+IlWJaXIUP4Z1Ffr7aBMLKKCfHCGK/p8tOfmn8scqzNjh1cNEmNUAmCIEIVEi/+0NSEcNGTR295GTSodc3uxMWstpb/ia7NABcvIp1ZpqyMP2+zBVZ4TIiKU6dauo0At+LFa8yLHJwKuLe8MMabCy5dqrpPfEF2GVksxpYXh4OnG4vjv3CBv09fiM9sLBbV+iJq3BgJS/nzmjaNFzAMxrHIwmnaNPPjagiCINoBEi/+IDcxlAN2gdZfDLt3V4NH//UvreUFMG5kKFxGaWmBuQI8uY0AYMwYfnvwoKbnks9uI/1+AFW8NDTwAnEiFVvMxRfkTCPA2PLywQdqgCpjwN/+plpd+vVTK9gGgxkztI+9iZdguIwEcoC3bIEiCIIIYUi8+IPr4siiorjQkN1GrRUvchyEkXgR7iqZ1sS7AIBoNVBTo7Y3kMVLZiYXKA6H+npzs+o6C8TyEh2tvl5QoIomf4rWyZlGQEvLi9MJPP+89rVt24IfrCvIytKKyfYUL8LyEhfXuoJ3BEEQHQgSL/4g+gCJC6KZlhfAs3gxinsR9TsCyTQCuIgQbiIhTmS3kVHQruya8dXyou/HJESH3PjRXQDvE0/wTt1y00XZbQS0tLxs2sRTvGNjVRGzbVvwg3UF8fG8SrHA6PMRwnfIkJbrYyZCqN19d0tRSRAEEaKQePEDixAvogFijx7qhdqMX/PiglZR0TaWF0B16YiYGtnyAqiuo717+a0rFod16+Y+xke+SKaltRQzQrzk5qrPGVlezp/nDSX/+U/eZ0ngzm0khNXvf89vH3wQuPlmfn//fu46AoJveQHUuJfISB44q+fGG7nIeeSR4B7HggU8TXr16uDuhyAIog0h8eIPwm0kLC9hYcAf/8h/3Qfa00hGWF6Ki7nrA1AvzEaWFzPEi74EvWx5AVpYXixCILizugBasSLHuwjEPEVAK2Bsedm4kdeZAdRAW3lbYXmR3UaXLvHMGoBfuFNTeTA1Y8CxY/z5thAvN9/Mz48RI7QNOAWjR3OBeu+9wT0Ou527i4IREEwQBNFOhLf3AYQUercRAMyda9744qJeVMRv7Xb+q13ukSMTaGsAGVlc2O3c1SIjxMv+/Tz2RWRBCVFlhCxejPrzCIuJjJHlZf169b6IjQHUmBe926i+nruHGONp68JdM3myuqaRka1LafeVkSOBv/+dW54IgiAIUyHLix+0cBuZjV68yHU69JaXigo1hkPfMsAfZPGSmtrSStC/P59vQwO3XAi3kSfLi+w2MrK8yOJFjlcRvZUAbpUQBdwAreWltFQ9XkD7eYgu2Ndfrz43ebJ6f8gQtbptsPnJT4Ibz0IQBNFFIfHiDyIgVLa8mImIeRGVWRMS1J5AsuWFMZ72eukSMGwYD2gNFFlc6F1GAHd9iG7JR46obiNfLS/exMvMmWpmjrCoADyYV7RdALSWF727zGpVLUZG4mXSJPV+sIN1CYIgiKBD4sUfXJYXpnetmIWwvAgrS3y8seXl9deBzz/nLpD3329dcTy95cUISbwobiNfLS/e3EY//anq/pFdRx9/zG9FFpewvDBm3M9JCEpRcVgWL6mpaj+ftoh3IQiCIIIKiRd/EG6jYIsXgWx5EeKlqAj49a/5/dWrW38xlt0a7sSLEBCHD/vmNvLH8nLddS1bBly6BGzezO8/9BC/FZaXCxfUgn1yPIlsCUpN5e4umaef5haqO+5wf9wEQRBESEDixQ8swXYbGYkXYcUQbqMNG/jFe+JE9cLeGqKiVNFi5DYC/HcbWa382H7xC17NVo+YZ0wMd3vpO2p/9hmPfxk2TI1XEeJFWF169tSKJPkzuf76lrE7c+cCe/a0LriZIAiC6BBQtpE/tJXbSJCQoFoZhOVFuG3GjDFOwQ2Evn25OHCXGSMsL8eOqWLEk+UFANascf/auHE81mXqVB48q7e8HDrEb6dMUYVVdTVw+bL7wnyymJJdRgRBEESng8SLPwTbbRQXx+NXRNaNSJMGVMtLMKw/S5YAOTlqQTc9GRncSlJTA4urWB3zZHnxht0OfPGF+lhveTl9mt/26cMziex2Lt7Ky93XtiHxQhAE0WUgt5EfBN1tJPc3AoxTpYNxDD/7GQ+QFYGzRsd11VX8rkhT9mZ58Qe95eXMGX7bpw/ft7C+yOJFb3kR6xEXx91NBEEQRKeFxIs/iK7SwbK8AC3Fiz5VOtgCyh363k3BFC/C8pKZyW9FLE5ZmfvCfMLyct11POaGIAiC6LSQePEH0R6gLcVLW1hefEEE7bpoldtIj+w2qq9X06JFppIvlpc5c3hVWzOCmAmCIIgODcW8+ApjqnBoK/ESH9/1LC8lJfx+t25qQ0MhXjxZXq65Bti3z7xjIgiCIDosZHnxlbo6WESTwGAKB1FlF+jQlhdTxYtseZGDdUU2lXAbeQrYJQiCILoMJF58RbiMLBaeeRMsOmrMS+/eyrE0RUYCERHmjS1bXmTxIhCWl5Mn1VRxvduIIAiC6DKQePEVl2hostvNq69ihBAvVquaJgxwy0tzsxo03NbiRepx5DBbvAnx0tioNpsUwbqAanlxpWkjOrrt508QBEF0GEi8+EqPHmhesgQnf/az4O5HiJeEBC6SZMuLEC5A+1y8hXjp1s3ccaOj1XkWFPBbI8uLmH96enAFJEEQBNGhIfHiKykpcK5Yge/uvDO4+xE9eYTlQba8CJdRZCT/a2tcQbuNwXCbCdFWWMhvjSwvAop3IQiC6NKQeOlojBgBfPIJ7xYNaHsbtVe8i2DWLLC4OJwbPdr8sYXrSLRDkC0vycncbSWgeBeCIIguDaVKd0TkMv1yV+n2Fi9Dh6KpogLH8/Iw0Oyx5U7TgNbyYrVyy0xFBX9MlheCIIguDVleOjpGbqP2DFYNC9IpI2dZRUVpU8YBNe4FIPFCEATRxSHx0tGRA3Y7gngJFrLlRa7xIpDFC7mNCIIgujQkXjo6suVFdJjujOJFtrzILiOBHLRLlheCIIguDYmXjo6wvADAuXP8tjOKF73lRQ9ZXgiCIAgXJF46OsLyAvDePkDnFC++Wl6s1pbxMARBEESXgsRLRyc8HLDZ+P3OLF58tbykpXEBQxAEQXRZSLyEAsL60pnFizfLy7XX8p5SWVltd0wEQRBEh4TqvIQC0dFAVRXvqgx0TvHizfLSuzfvOt0elYUJgiCIDgWJl1BAWF5EkbbOKF569gRuuonfl4NzZaKi2u54CIIgiA4LiZdQQGQcNTXx284oXiwWIDe3vY+CIAiCCAEo5iUUkDOOgM4pXgiCIAjCR4ImXlatWoUJEyYgOjoaPXr08Ok98+bNg8Vi0fzNnDkzWIcYOpB4IQiCIAiFoLmNGhsbcdttt2H8+PF48803fX7fzJkz8fbbbyuPIylAU1uoDiDxQhAEQXRpgiZeVqxYAQDIycnx632RkZFIkUvBE2R5IQiCIAiJDhewu23bNiQnJyM+Ph5Tp07Fc889h549e7rdvqGhAQ0NDcrjqqoqAIDD4YDD4TD12MR4Zo/rDWtUlOLfYxERaLJagTY+BkF7rUFHoavPH6A1AGgNAFoDgNbA7Pn7M46FMcZM2asbcnJysGjRIlwSTQU98OGHHyI6Ohp9+/ZFcXExnnrqKcTExGDXrl2wuqmq+uyzzypWHpn3338f0Xp3S4gy8g9/QOaWLQCAhrg45L3zTjsfEUEQBEGYS21tLe68805UVlYiNjbW47Z+iZcnn3wSzz//vMdtjh49isGDByuP/REvek6ePIn+/ftjy5YtmDZtmuE2RpaXjIwM/Pjjj14n7y8OhwObN2/G9OnTYRMl+9uAsEcegfW11wAAbMAANB050mb71tNea9BR6OrzB2gNAFoDgNYAoDUwe/5VVVVITEz0Sbz45TZ67LHHMG/ePI/b9OvXz58hvY6VmJiIEydOuBUvkZGRhkG9NpstaCdTMMc2pFs35a4lLq5D/JO0+Rp0MLr6/AFaA4DWAKA1AGgNzJq/P2P4JV6SkpKQJPegCTJnz57F+fPnkequ4mpXQXZ/UbAuQRAE0cUJWp2XkpISFBYWoqSkBM3NzSgsLERhYSFqamqUbQYPHowNGzYAAGpqarB48WLs3r0bp0+fxtatWzF79mwMGDAAM2bMCNZhhgZythGJF4IgCKKLE7Rso2XLluEdKbB01KhRAID8/HxMnjwZAFBUVITKykoAgNVqxYEDB/DOO+/g0qVLSEtLQ3Z2Nn7zm99QrReyvBAEQRCEQtDES05OjtcaL3KssN1ux5dffhmswwltyPJCEARBEArU2ygUIPFCEARBEAokXkIBchsRBEEQhAKJl1CALC8EQRAEoUDiJRQgywtBEARBKJB4CQXI8kIQBEEQCiReQgGyvBAEQRCEAomXUIAsLwRBEAShQOIlFCDLC0EQBEEoBK1IHWEisbFARAQQFgbEx7f30RAEQRBEu0LiJRSw24ENGwCrFejqrRIIgiCILg+Jl1Dhxhvb+wgIgiAIokNAMS8EQRAEQYQUJF4IgiAIgggpSLwQBEEQBBFSkHghCIIgCCKkIPFCEARBEERIQeKFIAiCIIiQgsQLQRAEQRAhBYkXgiAIgiBCChIvBEEQBEGEFCReCIIgCIIIKUi8EARBEAQRUpB4IQiCIAgipCDxQhAEQRBESNHpukozxgAAVVVVpo/tcDhQW1uLqqoq2Gw208cPBbr6GnT1+QO0BgCtAUBrANAamD1/cd0W13FPdDrxUl1dDQDIyMho5yMhCIIgCMJfqqurERcX53EbC/NF4oQQTqcTpaWl6N69OywWi6ljV1VVISMjA99//z1iY2NNHTtU6Opr0NXnD9AaALQGAK0BQGtg9vwZY6iurkZaWhrCwjxHtXQ6y0tYWBjS09ODuo/Y2NgueaLKdPU16OrzB2gNAFoDgNYAoDUwc/7eLC4CCtglCIIgCCKkIPFCEARBEERIQeLFDyIjI7F8+XJERka296G0G119Dbr6/AFaA4DWAKA1AGgN2nP+nS5glyAIgiCIzg1ZXgiCIAiCCClIvBAEQRAEEVKQeCEIgiAIIqQg8UIQBEEQREjR5cTLN998g5tuuglpaWmwWCzYuHGj5vWamhosXLgQ6enpsNvtGDJkCNauXavZZvLkybBYLJq/+++/X7NNSUkJZs2ahejoaCQnJ2Px4sVoamoK9vR8orVrcPr06RbzF3/r169XtjN6/cMPP2yrabrF2/wrKiowb948pKWlITo6GjNnzsTx48c129TX12PBggXo2bMnYmJi8G//9m+oqKjQbBPK54C3Nbhw4QIeeughDBo0CHa7Hb1798bDDz+MyspKzTgd9RwAzDkPOvt3gbc1CPXvgt/97ncYO3YsunfvjuTkZMyZMwdFRUWabcz6X9+2bRtGjx6NyMhIDBgwADk5OcGenk+YsQb79+/Hz3/+c2RkZMBut+Oqq67CK6+8ohlj27ZthudBeXl5QMfd5cTL5cuXcfXVV+O1114zfP3RRx9FXl4e3nvvPRw9ehSLFi3CwoULkZubq9nuvvvuQ1lZmfK3evVq5bXm5mbMmjULjY2N2LlzJ9555x3k5ORg2bJlQZ2br7R2DTIyMjRzLysrw4oVKxATE4MbbrhBM9bbb7+t2W7OnDnBnp5XPM2fMYY5c+bg5MmT+OSTT7Bv3z5kZmYiKysLly9fVrZ75JFH8Omnn2L9+vXYvn07SktLceuttyqvh/I54MsalJaWorS0FC+88AIOHTqEnJwc5OXl4Z577mkxXkc8BwBzzgOg834X+LIGof5dsH37dixYsAC7d+/G5s2b4XA4kJ2dbfr/+qlTpzBr1ixMmTIFhYWFWLRoEe699158+eWXbTpfI8xYg7179yI5ORnvvfceDh8+jKeffhpLlizBH/7whxb7Kyoq0pwHycnJgR0468IAYBs2bNA8N3ToULZy5UrNc6NHj2ZPP/208njSpEnsV7/6ldtxP//8cxYWFsbKy8uV59544w0WGxvLGhoaTDl2swh0DfSMHDmS/fKXv/Q6dkdDf4xFRUUMADt06JDyXHNzM0tKSmLr1q1jjDF26dIlZrPZ2Pr165Vtjh49ygCwXbt2McZC+xzwZQ2M+Oijj1hERARzOBxux+6oBLoGnfm7INDzIFS/Cxhj7Ny5cwwA2759O2PMvP/1xx9/nA0dOlSzr9tvv53NmDEj2FPym0DWwIgHH3yQTZkyRXmcn5/PALCLFy+acpxdzvLijQkTJiA3Nxc//PADGGPIz8/HsWPHkJ2drdnuz3/+MxITEzFs2DAsWbIEtbW1ymu7du3C8OHD0atXL+W5GTNmoKqqCocPH26zuQSKr2sg2Lt3LwoLCw1/dS9YsACJiYm49tpr8dZbb/nU6rw9aWhoAABERUUpz4WFhSEyMhI7duwAwOfrcDiQlZWlbDN48GD07t0bu3btAhDa54Ava2BEZWUlYmNjER6ubZkWaucA4N8adNbvgkDOg1D/LhBuz4SEBADm/a/v2rVLM4bYRozRkQhkDdyNI8aQGTlyJFJTUzF9+nT8/e9/D/g4O11jxtby6quvYv78+UhPT0d4eDjCwsKwbt06TJw4UdnmzjvvRGZmJtLS0nDgwAE88cQTKCoqwl//+lcAQHl5ueZEBqA8DtS/15b4sgYyb775Jq666ipMmDBB8/zKlSsxdepUREdH46uvvsKDDz6ImpoaPPzww20xjYAQ/5RLlizBH//4R3Tr1g0vv/wyzp49i7KyMgD8M4yIiECPHj007+3Vq5fy+YbyOeDLGuj58ccf8Zvf/Abz58/XPB+K5wDg+xp05u+CQM6DUP4ucDqdWLRoEa677joMGzYMgHn/6+62qaqqQl1dHex2ezCm5DeBroGenTt34i9/+Qs2bdqkPJeamoq1a9fimmuuQUNDA/70pz9h8uTJ2LNnD0aPHu33sZJ40fHqq69i9+7dyM3NRWZmJr755hssWLAAaWlpivKUv6CHDx+O1NRUTJs2DcXFxejfv397Hbpp+LIGgrq6Orz//vt45plnWowjPzdq1ChcvnwZv//97zvUF5Yem82Gv/71r7jnnnuQkJAAq9WKrKws3HDDDR3yl2Iw8HcNqqqqMGvWLAwZMgTPPvus5rVQPAcA39egM38X+HsehPp3wYIFC3Do0CGP1sXOjhlrcOjQIcyePRvLly/XWOsHDRqEQYMGKY8nTJiA4uJivPzyy/jf//1fv/dDbiOJuro6PPXUU3jppZdw0003YcSIEVi4cCFuv/12vPDCC27fN27cOADAiRMnAAApKSktotHF45SUlCAdvTn4uwYff/wxamtrMXfuXK9jjxs3DmfPnlXM0R2VMWPGoLCwEJcuXUJZWRny8vJw/vx59OvXDwD/DBsbG3Hp0iXN+yoqKpTPN5TPAcD7Ggiqq6sxc+ZMdO/eHRs2bIDNZvM4bqicA4DvayDTmb4LAP/WIJS/CxYuXIjPPvsM+fn5SE9PV54363/d3TaxsbEdxurSmjUQHDlyBNOmTcP8+fOxdOlSr/u89tprlf8VfyHxIuFwOOBwOBAWpl0Wq9UKp9Pp9n2FhYUAuFkMAMaPH4+DBw/i3LlzyjabN29GbGwshgwZYv6Bm4i/a/Dmm2/i5ptvRlJSktexCwsLER8fHzJNzOLi4pCUlITjx4+joKAAs2fPBsC/0G02G7Zu3apsW1RUhJKSEowfPx5AaJ8DMu7WAOAWl+zsbERERCA3N1cTG+GOUDsHAM9roKczfRfI+LIGofhdwBjDwoULsWHDBnz99dfo27ev5nWz/tfHjx+vGUNsI8ZoT8xYAwA4fPgwpkyZgrvvvhurVq3yad+FhYXK/0ogB96lqK6uZvv27WP79u1jANhLL73E9u3bx86cOcMY49kDQ4cOZfn5+ezkyZPs7bffZlFRUez1119njDF24sQJtnLlSlZQUMBOnTrFPvnkE9avXz82ceJEZR9NTU1s2LBhLDs7mxUWFrK8vDyWlJTElixZ0i5z1tPaNRAcP36cWSwW9sUXX7TYR25uLlu3bh07ePAgO378OHv99ddZdHQ0W7ZsWZvM0RPe5v/RRx+x/Px8VlxczDZu3MgyMzPZrbfeqhnj/vvvZ71792Zff/01KygoYOPHj2fjx49XXg/1c8DbGlRWVrJx48ax4cOHsxMnTrCysjLlr6mpiTHWsc8Bxlq/Bl3hu8CX/wXGQve74IEHHmBxcXFs27ZtmnO4trZW2caM//WTJ0+y6OhotnjxYnb06FH22muvMavVyvLy8tp0vkaYsQYHDx5kSUlJ7K677tKMce7cOWWbl19+mW3cuJEdP36cHTx4kP3qV79iYWFhbMuWLQEdd5cTLyJdS/939913M8YYKysrY/PmzWNpaWksKiqKDRo0iL344ovM6XQyxhgrKSlhEydOZAkJCSwyMpINGDCALV68mFVWVmr2c/r0aXbDDTcwu93OEhMT2WOPPaZJIW1PWrsGgiVLlrCMjAzW3NzcYh9ffPEFGzlyJIuJiWHdunVjV199NVu7dq3htm2Nt/m/8sorLD09ndlsNta7d2+2dOnSFmmtdXV17MEHH2Tx8fEsOjqa3XLLLaysrEyzTSifA97WwN37AbBTp04xxjr2OcBY69egK3wX+PK/wFjofhe4O4fffvttZRuz/tfz8/PZyJEjWUREBOvXr59mH+2JGWuwfPlywzEyMzOVbZ5//nnWv39/FhUVxRISEtjkyZPZ119/HfBxW1wHTxAEQRAEERJQzAtBEARBECEFiReCIAiCIEIKEi8EQRAEQYQUJF4IgiAIgggpSLwQBEEQBBFSkHghCIIgCCKkIPFCEARBEERIQeKFIAiCIIiQgsQLQRAEQRAhBYkXgiAIgiBCChIvBEEQBEGEFCReCIIgCIIIKf4fsrFqRcJs50MAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ------------------------ Let's now move on to an ensemble member ------------------------\n",
    "en   = 10                                                                # 1 - 200\n",
    "ds   = read_dcenti_google_cloud(en)                                      # Read data\n",
    "ts   = ds.ts.values                                             # Subset temperature\n",
    "lsat = ds.lsat.values                                           # Subset temperature\n",
    "sst  = ds.sst.values                                            # Subset temperature\n",
    "\n",
    "GL = np.zeros((temperature.shape[0],3))\n",
    "for ct in np.arange(temperature.shape[0]):\n",
    "    GL[ct,0] = calculate_global_mean_temperature(ts[ct,:,:])      # Calculate global mean while weighting grids by cosine of latitude\n",
    "    GL[ct,1] = calculate_global_mean_temperature(lsat[ct,:,:])    # Calculate global mean while weighting grids by cosine of latitude\n",
    "    GL[ct,2] = calculate_global_mean_temperature(sst[ct,:,:])     # Calculate global mean while weighting grids by cosine of latitude\n",
    "\n",
    "GL_ann = GL.reshape(int(temperature.shape[0]/12),12,3).mean(axis = 1)      # Calculate annual mean of monthly mean\n",
    "plt.plot(np.arange(1850,yr_end+1),GL_ann[:,0],color='k', label = 'TS');  \n",
    "plt.plot(np.arange(1850,yr_end+1),GL_ann[:,1],color='r', label = 'LSAT');  \n",
    "plt.plot(np.arange(1850,yr_end+1),GL_ann[:,2],color='b', label = 'SST');  \n",
    "plt.grid('on')\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "957b2b26-db06-4f92-93b4-ee00f39bf5cd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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0yZY5IiIiSUXLo0ePWL58OXfv3mXcuHFMmTKFNm3a0K9fPwoXLpwsOYUwZ1J8CCFSjNFo5NSpU3G9Gjt27ODu3bsvPcfJyYmyZctSrVo1qlatSvny5XFzc9Mo8at0Oh2urq64urqSNm3aRH2OXq+nQoUK6PV6Zs6cyd69e1m8eDGLFy+mTp069O/fnzp16qCTBYSEjZDiQwiRbAwGAydPnozr1QgMDOT+/fsvPcfZ2Zny5cvHXUYpW7Ysrla4GYa9vT0NGjSgZcuW7N27l+nTp/Prr7+yZcsWtmzZQqFChejfvz+tW7fG2dyXpxTiPUnxIYQwGYPBwLFjx+J6NQIDA3n48OFLz3F1daVChQpxl1HKlCkTNyDTVpQvX541a9Zw+fJlZs6cycKFCzlx4gSdOnVi6NCh9OzZk27dupEuXTqtowqRLKT4EEK8s5iYGIKCgl4qNv47u8TNzY2KFSvGXUYpXbo0Tk5O2gQ2M9mzZ8ff3x8/Pz/mz5/PrFmzuHHjBiNHjmTChAl06NCBvn37kjdvXq2jCmFSUnwIIRItOjqao0ePxl1G2bVrF0+fPn3pOR4eHlSqVCnuMkrJkiVx1GoDCQuROnVqBg4cSN++fVmzZg3Tpk3jyJEjzJs3j3nz5tGgQQMGDBhAlSpVZFyIsApSfAghXkuv13P27FlOnjzJzp072b17N8+ePXvpOV5eXlSuXDnuMkqJEiVkddF35OjoSOvWrfn0008JDAxk2rRp/P7773FHiRIlGDBgAC1atJCCTlg0+QkhhHjF/fv3+e6775gzZ84rA0RTp05NlSpV4oqNYsWKyUqfJqbT6eLa9+zZs/j7+7NkyRKOHDlCmzZtGDx4ML179+aLL74gderUWscVIsnstA4ghDAfZ8+epWvXrmTJkgU/Pz/u37+Pp6cnDRs2xN/fn6NHj/LgwQN+++03+vfvT8mSJaXwSGZ58+Zl7ty5XL9+nbFjx+Lt7c2NGzcYNGgQvr6+9O3b97VLzAthrqT4EMLGGY1Gtm/fToMGDciXLx8//PADERERlCpVimXLlrFkyRLWrl1Lnz59pJdDQ+nSpWPEiBFcuXKFRYsWUahQIUJDQ5k5cya5cuXik08+Yd++fVrHFCJRpPgQwkbp9XpWrlxJqVKlqF69Ohs2bECn09GwYUN27NjBgQMHaNmypfkXG3fvwpkzEB2tdZIU4eLiQseOHTl27Bh//vknderUwWAwsGbNGsqXL0/FihX55ZdfiImJ0TqqEK8lxYcQNubp06dMnTqVnDlz0qZNG44cOYKLiwvdunXjzJkz/Pbbb5YxqyI8HIYNA19fyJ8fPD2hVCno1AlmzoRt2+DRI61TJhudTkfdunXZvHkzx44do2PHjjg5ObFnzx6aN29O7ty5mTVrFqGhoVpHFeIVMuBUCBtx7do1Zs6cyfz58+NmrGTIkIGePXvSvXt3y1rQasMG6NULrlxRt11cICICDh9Wx4syZ4YiRaBoUXUUKQK5c4MVzcgpXLgwixYtYsKECcyZM4fvvvuOy5cv06dPH0aNGkXXrl3p2bMnmTNn1jqqEID0fAhh9Q4dOsSnn35Kjhw5mD59Os+ePSN//vwsWLCAq1evMnLkSMspPK5dgyZNoEEDVXj4+sK6dRAWBufPwy+/wNdfQ6NGkD27+pwbN2DTJpg4EVq1ggIF4ntJOneGWbNg+3ZI5M605ixjxoyMHTuW69ev891335E7d26ePHnC5MmTyZ49O+3atePo0aNaxxRCej6EsEYGg4ENGzYwbdo0AgMD4+6vUaMGAwYM4KOPPsLOzoL+9tDr1aUUPz9VaDg4QL9+qtCI3eE2Vy51NG0a/3lPn8Lx43DsGAQHq4/Hj6vXSKiXxNc3vpck9mPu3GDu417+w83Nje7du9O1a1c2bNjA9OnT2bFjB8uXL2f58uVUr16d/v37U79+fct6HwirIcWHEFbk+fPnLF26lOnTp3Pu3DkAHBwcaNWqFQMGDKBYsWLaBnwXu3dD9+6qaACoVAnmzoVChd7+ualSqedXqhR/n8EAFy++XJAEB6uelOvX1bFxY/zzXVzU13qxIClSBD74wKTfZnKws7OjYcOGNGzYkEOHDjFjxgx++ukntm3bxrZt28iXLx/9+vWjXbt2VrmZnzBfUnwIYQXu3bsXd63/wYMHAKRKlYquXbvSq1cvy7zW//AhDB4MCxeq22nTwpQp8Nln8D5/rdvZqd6M3LmhWbP4+2N7SYKDX+4lCQ+HQ4fU8SJf35fHkRQtqnpezLSXpFSpUqxYsYJJkyYxa9YsfvjhB86cOUPXrl0ZPnw4X375JT169CBDhgxaRxU2QIoPISzY6dOnmT59OsuWLSMyMhKAbNmy0bdvXzp16oSnp6fGCd+BwQBLlsCgQaoAATU2Y/JkVYAkl4R6SWJi4NKllwuS4GC4ejW+l2TDhvjnu7om3Evi7p58uZPI19eXb775hq+//pqFCxfi7+/P1atXGTNmDLNmzWLRokU0adJE65jCyknxIYSFMRqNbNu2jWnTprFp06a4+8uWLcuAAQNo0qSJ5e6tcuKEusSya5e6XagQzJsHFStqk8fePr6XpHnz+PufPInvJYktSI4fh+fP4eBBdbzAIUsWCpQoAXXrgpnsyeLp6Unfvn3p2bMnAQEBTJgwgaCgIJo2bUqPHj2YOnUqLi4uWscUVkpGGglhIfR6PcuXL6dkyZLUrFmTTZs2odPpaNKkCbt27WLv3r20aNHCMguPsDDV01G8uCo83N3hm2/gyBHtCo83SZ0aKleGnj3hhx9g/3549gzOnoWff4YRI9SMnCxZANBdu0budeuw69ULjEZts/+Hg4MDLVq04MCBAwwaNAiAOXPmUK5cOc6ePatxOmGtLPCnlBC25cmTJ8yfP5+ZM2dy8+ZNAFxdXenYsSN9+/Yld+7cGid8T7/9ptbsuH5d3W7SRM1s8fXVNldS2dtDnjzqaNEi/v4nT4j++Wfsu3fHfsECSJdOTfs1M46OjkyePJnq1avTvn17goODKVmyJHPmzKFDhw5axxNWRno+hDBTV65coV+/fvj6+jJo0CBu3ryJt7c348aN4/r168yZM8eyC48rV6BhQ2jcWBUeWbPC77/Dr79aXuHxJqlTY+zYkaDu3dXtSZPUwFkz9dFHHxEUFESNGjUICwvjs88+o3379nEL0wlhClJ8CGFmYvdUyZkzJ/7+/oSGhlKoUCEWLVrE1atXGT58OGmTc+BlcouKUoNHCxRQxYaDAwwdCqdOwf/+p3W6ZHOtdm1iYns8Bg+G+fO1DfQGPj4+bNmyhXHjxmFnZ8eyZcsoWbKkLFAmTEaKDyHMQExMDOvWraNy5cqULVuWn3/+GYPBQO3atfnzzz/j9u5wdnbWOur7CQxU4zqGDFGDM6tWVYM1J0wANzet0yU7w4ABqtAC6NpVjQ8xU/b29gwfPpwdO3aQOXNmzp8/T7ly5Zg9ezZGMxu3IiyPFB9CaCg8PJzvvvuOfPnyxQ0cdXR0pEOHDgQHB7Nlyxbq1q1r/pu8vc39+2p9jqpVVQ9H+vTw449q87cCBbROl7LGj1eFh9EIbdvCn39qneiNKlWqRHBwMI0aNSIqKorevXvTpEkTHlnxpn0i+UnxIYQGwsLCGD16NFmyZKFHjx5cuHCB1KlTM3ToUK5cucKSJUsoUqSI1jHfn8GgZoPkzauKDVC/eM+cgfbtwdKLqneh08GcOdCypVo2vmlTtYqrGUuTJg0BAQHMmjULJycnfvvtN4oVK8au2CnRQiSRFB9CpCCj0ciaNWvInz8/fn5+PHz4kBw5cjBr1iyuX7/OhAkT8PHx0TqmaQQHqwW7unZVm7YVLQp796p1O9Kk0TqdtuztYelSqFdPXX76+GPVXmZMp9PRq1cv9u3bR+7cubl+/TpVq1Zl3LhxxMTEaB1PWBgpPoRIISdPnqRmzZp88sknXL9+naxZs7J69WrOnTtHr1698IjdIM3SPXsG/ftDyZKq2PDwgBkz1PLk5cppnc58ODnB2rVqHZOnT9UCZOfPa53qrYoXL87hw4dp27YtBoOBkSNHUqdOHW7fvq11NGFBpPgQIpk9ffqUfv36UbRoUbZt24aLiwujRo3i1KlTtGzZEnsz3QskyYxGtaV9/vyq2IiJUauCnjkDffuqWS3iZW5uann2okXh7l2oXRv+XcvFnHl6erJs2TKWLFmCm5sb//zzD0WLFuVPMx+/IsyHFB9CJBODwcCSJUvIkycP/v7+xMTE0LhxY06dOoWfnx9u1jS749IldemgeXP1yzNHDvjjD1izBj78UOt05i11ati8WS3hfvWqKkD+3RzQ3HXo0IEjR45QtGhR7t+/T7169Rg8eDB6vV7raMLMSfEhRDI4dOgQFStWpGPHjty7d488efLw559/EhAQQPbs2bWOZzqRkTBuHBQsqIoNR0e1tPiJE/DRR1qnsxze3rB1qyrUTp+G+vXV5SsLkDdvXvbt20ePHj0AmDJlCpUrV+by5csaJxPmTIoPIUzowYMHdOnShTJlyrBv3z48PDyYMmUKx48fp27dulrHM61t29TlgpEjISICatRQm6uNHat2dxVJkzWrKkDSplUb0zVqpNrVAri4uPDtt9/y66+/kjp1avbv30+xYsVYs2aN1tGEmZLiQwgTiI6OjlvufP78+RiNRtq0acPZs2cZOHAgTk5OWkc0nbt31foUNWqojdS8vWHFCvjrLzWlVry7/PnVuh+enqq4a9UKoqO1TpVoTZo0ISgoiPLlyxMSEsInn3xCt27deP78udbRhJmR4kOI97Rz505KlixJz549efLkCUWLFiUwMJDly5dbz7RZUANI585VBcaKFWq9ii+/VANKW7e2zTU7kkOpUrB+PTg7q033OndW66VYiKxZs7Jjxw6GDh2KTqfj+++/p0yZMpw6dUrraMKMSPEhxDu6efMmbdq0oUqVKhw7dowPPviAOXPmcOjQISpXrqx1PNM6ehTKl1fFxtOnUKKE2kZ+zhw1YFKYVrVqaun12PVA+vVTs4kshKOjIxMmTGDz5s14e3tz4sQJSpUqxcKFC2VpdgFI8SFEkkVFRTFlyhTy5s3LypUr0el0dOnShXPnzvHll1/iYE1TSkNCKLRgAQ7ly6txCF5eMGsWHDgApUtrnc66NWwIS5ao81mz1FgaC1O7dm2Cg4OpU6cOz58/5/PPP6d169aEhIRoHU1oTIoPIZJg8+bNFC5cmMGDBxMWFka5cuU4ePAg33//PenSpdM6nmk9eIBD6dLk3LABncGgxh+cOQO9eqm/yEXya9tWFR4Ao0bFn1sQb29v/vjjDyZNmoS9vT2rV6+mRIkSHDp0SOtoQkNSfAiRCJcvX6Zx48Z89NFHnDt3jgwZMrBkyRJ2795NyZIltY5negYDtG+P7vJlwtOnJ3rTJli1CjJl0jqZ7enVC0aPVud9+sCyZdrmeQd2dnYMHjyYnTt3kjVrVi5evEiFChWYMWOGXIaxUVJ8CPEG4eHhjBo1ivz58/Pbb79hb29Pv379OHfuHB06dMDOzkr/C02dCn/8gdHFhf3Dh2OsVUvrRLZt5EhVeAB07KgGpFqg8uXLc/ToUZo2bYper6d///40aNCABxayqJowHSv9ySnE+zEajfz666/kz5+fMWPGEBkZSY0aNQgODmb69OmkSpVK64jJZ88eGDYMgJjp0wnJlk3bPELNJJo+HTp0ULOOPvlETcW1QB988AFr167lu+++w9nZmY0bN1K0aFF27NihdTSRgpJcfAQGBtKgQQN8fHzQ6XSsW7fuleecPn2ahg0bkipVKtzd3SldujTXrl0zRV4hkt3p06epU6cOzZo149q1a/j6+rJmzRr++usvChYsqHW85PXwoRrbERMDn36KsXNnrROJWHZ2sGCBWnwsMlINSLXQcRM6nY7u3btz4MAB8uXLx61bt6hRowZ+fn6yQ66NSHLxERYWRtGiRZkzZ06Cj1+8eJFKlSqRL18+tm/fzrFjxxg5ciQuLi7vHVaI5BQSEsJXX31FkSJF+Ouvv3B2dmbEiBGcPn2a5s2bo7P2dSyMRtWlf/262mfk++9l7Q5z4+AAq1dD9eoQGqqWsD99WutU76xIkSIcOnSIjh07YjAYGD16NDVq1ODGjRtaRxPJLMlzAuvVq0e9evVe+/jw4cOpX78+U6ZMibsvZ86c75ZOiBRgNBpZvnw5gwYN4s6dOwA0aNCAGTNm2NZ7d8YM+P13tbjVzz+rVTZlgzDz4+KiFh+rWVNNf65dG3btAgu9PObu7s6iRYuoWbMm3bp1IzAwkGLFirFkyRL+97//aR1PJBOTjvkwGAxs3LiRPHnyULduXTJkyEDZsmUTvDQjhDk4evQolSpVon379ty5c4dcuXKxceNG1q9fb1uFx759MHiwOp8xA4oV0zSOeAtPT7WRX4ECahfh2rXVsvcWrE2bNhw5coQSJUrw8OFDGjRoQP/+/YmKitI6mkgGJl0N6d69e4SGhjJp0iTGjRvH5MmT+fPPP2natCnbtm2jatWqr3xOZGQkkZGRcbdjF5/R6/U2uS1z7Pdsi9+7Kb2tHR8+fMioUaPi9mFxc3Nj2LBh9OnTB2dnZ9tq/8ePcWjVCl10NIZmzYjp3Dmux0Pej6aRLO3o5QUbN+JQrRq6Cxcw1qlD9F9/WfSKs9myZWPHjh0MGzaM2bNnM2PGDHbs2MHy5cvJlSuXvB9NJLnaMSmvpzO+xyRrnU5HQEAAjRs3BuDWrVt8+OGHfPrpp6xcuTLueQ0bNsTd3Z1Vq1a98hp+fn6Mjp3D/oKVK1fi5ub2rtGESFBMTAxbt25lxYoVPPt3y/LKlSvToUMH61skLDGMRspMnEimAwcI8/Zm+/TpRLu7a51KJIH77dtUGjoUlydPeJg/P3v9/IhxdtY61ns7cOAAs2fP5tmzZ7i6utK9e3eqVKmidSzxBuHh4bRu3ZqnT5/i5eX1xueatOcjXbp0ODg4UKBAgZfuz58/P7t27Urwc4YOHUr//v3jboeEhODr60udOnXeGt4a6fV6tm7dSu3atXF0dNQ6jsVKqB337t1Lnz59CAoKAqBgwYL4+/sn2CNnK+xmz8b+wAGMTk44/fYbdUqUeOlxeT+aRrK3Y+nSGGvVIu3p09RftIiYX34BC99JuX79+nz++ee0b9+eXbt2MX36dB48eED9+vVp0KCBvB/fQ3K9H5OybL5Jiw8nJydKly7N2bNnX7r/3LlzZM2aNcHPcXZ2xjmBKt3R0dGm31y2/v2biqOjIw8ePGDw4MEs+3dlyFSpUjF27Fi6d+9uXfuwJNXBgzBkCAC6qVNxLFv2tU+V96NpJFs7liwJGzdCrVrYbd6MXefOaudhC18GP3v27Gzbto0xY8Ywbtw4li5dyvbt2ylevDh58uTROp7FM/X7MSmvleQBp6GhoQQFBcX99Xj58mWCgoLi1vEYOHAgP/30E/Pnz+fChQt8++23/P7773z55ZdJ/VJCvJfo6GhmzJhB3rx5WbZsGTqdjs6dO3Pu3Dl69epl24XHkyfQsqUa29G0KfTsqXUi8b4qVICAAHB0hJ9+UjsQW8HS5Q4ODowZM4a///6bjBkzcu3aNSpVqsSePXu0jibehzGJtm3bZgReOTp06BD3nIULFxpz5cpldHFxMRYtWtS4bt26RL/+06dPjYDx6dOnSY1mFaKioozr1q0zRkVFaR3Fom3evNmYOXPmuPdn6dKljfv379c6lnkwGIzGZs2MRjAas2UzGh8/fu1T5f1oGinajj//bDTqdOrfd8iQ5P96Kejy5cvGHDlyGAGjk5OTcfny5VpHskjJ9X5Myu/vJP/pV61atbduBNSpUyc6deqU1JcW4r2FhoYycOBA5s2bB0D69OmZOHEiHTt2tN59WJLqu+/gl1/i/0K24NkRIgEtWqierS5dYNIk+OADGDRI61Qm8eGHHzJhwgRWrlzJ+vXradu2LWfOnGH06NHy/9vCyL+WsBqBgYEUKVIkrvCoV68eJ0+epHPnzvKDKdaRIxA7wHvKFChTRts8Inl88QVMnqzOBw+G+fO1zWNCLi4u/Pzzzwz+d12acePG0apVK8LDwzVOJpJCfiILi/f8+XP69+9PtWrVuHz5MlmyZGHz5s107dqV1PJXfbyQELUhWVSU2h8kdpdUYZ0GDYpfOK5rV7VqrZWws7Nj0qRJLF68GEdHR9asWUO1atW4ffu21tFEIknxISzagQMHKF68ODNmzMBoNNK5c2eOHz9O9erVtY5mXoxG1Q1/8SJkyQKLFsm+LbZg4kT17240Qtu2sHmz1olM6rPPPuOvv/4iTZo0HDx4kDJlysRNhhDmTYoPYZGioqIYMWIE5cuX5+zZs2TKlIkNGzawYMECm1wf5q2+/16N73BwUB/TpNE6kUgJOp0a4/PJJ/Ezm6xslkiVKlXYv38/efPm5caNG1SqVIn169drHUu8hRQfwuIEBwdTunRpxo8fj8FgoHXr1pw4cYKPP/5Y62jmKSgI+vZV5xMnQrlyWqYRKc3eHpYtUzvghodD/foQHKx1KpPKlSsXe/fupWbNmoSFhdG4cWOmTp361skRQjtSfAiLER0dzfjx4yldujTHjh0jbdq0rFmzhhUrVpBG/pJP2LNn6q/eyEj4+OP4wabCtjg5qRlOFSvC06dQty5cuKB1KpP64IMP+OOPP+jatStGo5GBAwfyxRdfyMZ0ZkqKD2ERzpw5Q8WKFRkxYgR6vZ5GjRpx8uRJmjdvrnU082U0QrducP48ZM4MP/4IMuvHdrm5wYYNULSo2gG3Vi21I64VcXR0ZO7cufj7+2NnZ8fChQupW7cujx490jqa+A/5SSTMmsFgYMaMGRQvXpwDBw6QKlUqli5dSkBAAN7e3lrHM28LF8LKlarbffVqSJtW60RCa6lTq0GnuXLB1atQuzY8eKB1KpPS6XT06dOH9evX4+Hhwfbt2ylXrhznzp3TOpp4gRQfwmxdunSJ6tWr079/fyIiIqhTpw4nTpygXbt26GSmxpsdPw69eqnzceNUd7sQAN7esHUrfPghnD6txoD8u8OzNfn444/Zs2cPWbJk4fz585QrV45t27ZpHUv8S4oPYXaMRiPff/89RYoUITAwEHd3d+bNm8eff/5J5syZtY5n/kJD1TiPiAg1yNBKVrcUJpQtG2zZonrDDh5U675ERGidyuQKFy7MgQMHKFeuHI8fP6ZOnTosWLBA61gCKT6Emblx4wYfffQR3bp1IywsjCpVqnDs2DG6du0qvR2J1aMHnDkDPj6wdKmM8xAJK1AA/vgDPDxg2zZo1Qqio7VOZXLe3t78888/tGrViujoaL744gu++uorYmJitI5m0+SnkjALRqORpUuXUqhQIbZs2YKLiwszZsxg27Zt5MiRQ+t4lmPJkviCY9UqSJ9e60TCnJUuDevXg7Mz/PabWgnVCrm6urJy5Ur8/PwAmDZtGk2aNCE0NFTbYDZMig+hubt379KkSRM6dOjA06dPKVOmDEePHqVv376yJ0tSnDyptlEHGDMGqlTRNo+wDNWrq6XX7e3VyrdWtgpqLJ1Ox6hRo1i1ahXOzs78/vvvVKpUievXr2sdzSbJT3ahqbVr11KwYEF+++03HB0dGT9+PLt37yZfvnxaR7MsYWFqnMfz52oGw9ChWicSlqRhw/gByn36qP1/rFSrVq3Yvn07GTJkIDg4mDJlynDgwAGtY9kcKT6EJh49ekTr1q1p0aIFDx8+pEiRIhw8eJBhw4bh4OCgdTzL06sXnDoFGTOq1Sylx0gk1ahR6jLd2bPw7bdap0lW5cqV48CBAxQuXJg7d+5QtWpVfraijfcsgfyEEilu48aNFCpUiFWrVmFnZ8fw4cM5ePAgRYsW1TqaZVq2DBYvVgXHypVqKqUQSZU6tVp+H2D0aLUQmRXLmjUru3bton79+kRERNCyZUvGjRsnS7KnECk+RIoJCQnh888/53//+x+3b98mb9687Nmzh3HjxuHk5KR1PMt05gx0767Ov/5aXb8X4l117AilSkFICAwbpnWaZOfl5cX69evp++/eRyNHjqRdu3ZEWOG0Y3MjxYdIEf/88w+FCxdm4cKF6HQ6+vXrx9GjRylbtqzW0SzX8+dqnEdYGNSoASNGaJ1IWDo7O5g1S50vWgQ2MBbC3t6eGTNmMHfuXOzt7VmxYgU1a9bk3r17WkezalJ8iGQVFhZGr169qFmzJteuXSN79uxs27aN6dOn4+rqqnU8y9anj1rJNEMGWLFCzVYQ4n2VLw/t2qnz3r3BYNA2Twrp1q0bf/75J6lSpWLPnj2ULVuWEydOaB3LaknxIZLNnj17KFasGN/+O3ita9euBAcHU7VqVY2TWYFVq2D+fNDpVOGRMaPWiYQ1mTRJLT62f78aU2QjatWqxb59+8iZMydXrlyhQoUK/PHHH1rHskpSfAiTi4iIYPDgwVSuXJkLFy7w4Ycf8ueffzJv3jw8PT21jmf5zp2DLl3U+YgRandSIUzJxwdGjlTnQ4aoMSA2Il++fOzfv58qVarw7Nkz/ve//zF79mwZiGpiUnwIkzpy5AilSpViypQpGAwG2rdvz4kTJ6hbt67W0axDRIQa5xEaClWrqumRQiSHPn3U7rd37qjNCW1I2rRp2bp1K5999hkGg4HevXvTs2dPoq1w+XmtSPEhTEKv1zN69GjKli3LyZMnyZAhAwEBAfz444+kTp1a63jWo39/CA5W6zGsXCnjPETycXYGf3917u+v1v+wIU5OTixatIjJkyej0+n47rvv+Pjjj3ny5InW0ayCFB/ivZ08eZJy5crh5+dHdHQ0zZo148SJEzRu3FjraNbl559h7lx1vmyZ6hoXIjl9/DHUrw96PfTrp3WaFKfT6Rg0aBC//PILbm5ubNmyhQoVKnDp0iWto1k8KT7EO4uJieGbb76hRIkSHDlyhA8++ICVK1eyZs0a0suGZqZ14QJ8/rk6HzoU5DKWSCkzZoCjo9oBd+NGrdNookmTJuzcuRMfHx9Onz5NmTJl2Llzp9axLJoUH+KdnD9/nipVqjBo0CCioqKoX78+J06c4NNPP0Wn02kdz7pERkLLlvDsGVSqpDaNEyKl5MkD/y7CRd++6v1og0qUKMGBAwcoUaIEDx8+pGbNmixdulTrWBZLig+RJFFRUfj7+1O0aFH27NmDp6cnCxYsYMOGDfjIZYDk8dVXcOQIpE2rptjK3jcipY0YoaZzX7gQPw7EBn344YcEBgbStGlT9Ho9HTp0YNiwYRhsZC0UU5LiQySKwWBg1apV5M+fn379+vH8+XOqV6/O8ePH6dy5s/R2JJdffonf5GvpUsicWds8wjZ5ecHkyep83Di4dUvbPBpyd3dnzZo1DP135+iJEyfSokULwsPDNU5mWaT4EG9kNBrZvHkzpUqVonXr1ly6dAlvb2/mzZvHX3/9RdasWbWOaL0uXYLOndX5wIFq4J8QWmnbFsqVU9O8hwzROo2m7OzsmDBhAkuWLMHR0ZFff/2VKlWqcMuGi7KkkuJDvNb+/fupUaMGH330EUePHsXT05OxY8dy4cIFunbtip1s2558oqKgVSt4+lQtdz1+vNaJhK17cd+XZctgzx5t85iBDh068Pfff5M2bVoOHz5MmTJlOHLkiNaxLIL89hCvOHPmDE2bNqVcuXJs374dJycn+vXrx6VLlxgxYgQeHh5aR7R+gwfDwYPwwQewerWabSCE1kqXhk6d1LkN7fvyJpUrV2b//v3ky5ePmzdvUrlyZX766SdZEfUtpPgQcW7cuMHnn39OwYIFCQgIwM7Ojs8++4zz588zffp00qVLp3VE2/Dbb/GD+n78EbJk0TSOEC+ZMEGNATl8GBYv1jqNWciZMyd79+6ldu3ahIeH06pVK6pVq8b+/fu1jma2pPgQPHr0iEGDBpE7d24WLlyIwWCgUaNGHDt2jMWLF5NFfvmlnCtX4LPP1Hn//tCggZZphHiVtzf4+anzoUNBVvwEIHXq1GzcuJHhw4fj7OxMYGAg5cqV45NPPuHChQtaxzM7UnzYsPDwcCZOnEiOHDn45ptviIiIoHLlyuzevZt169ZRsGBBrSPalthxHk+eQJkyMHGi1omESFjPnpAvH9y/D6NHa53GbDg6OjJu3DjOnz/PZ599hk6nY82aNeTPn5+ePXty7949rSOaDSk+bJBer2fevHnkypWLYcOG8fTpUwoXLsyGDRvYsWMHFSpU0DqibRo2TG1hnjo1/PQTODlpnUiIhDk6wsyZ6nz2bDh1Sts8ZsbX15fFixcTFBREvXr1iI6OZs6cOeTMmZOxY8cSFhamdUTNSfFhQwwGAz///DMFCxake/fu3L59m2zZsrFs2TKOHj3Kxx9/LOt1aGXDBpg2TZ0vXgzZsmkaR4i3qlMHGjWCmBi1A64MsHxFkSJF2LRpE3///TclS5YkNDSUr7/+mly5cvH999/b9C65UnzYiL/++osyZcrQsmVLzp8/T/r06Zk1axZnzpyhbdu22MvuqNq5fh06dFDnvXuDbMgnLMX06Wr327/+UgOlRYJq1KjBgQMHWLVqFdmzZ+fOnTt069aNwoULs27dOpucGSPFh5U7dOgQtWrVonbt2hw+fBgPDw/8/Py4ePEivXr1wtnZWeuItk2vV+M8Hj2CkiVhyhStEwmReDlywIAB6rxfP3j+XNs8ZszOzo5WrVpx+vRp/P39SZs2LWfOnKFJkyZUrlyZvXv3ah0xRUnxYaXOnTvHJ598QunSpfn7779xdHSkT58+XLx4kVGjRuHp6al1RAEwcqRarMnLC37+Wf0VKYQlGToUPvxQzdSKvXQoXsvZ2TnuZ/GwYcNwdXVl9+7dVKhQgaZNm3L27FmtI6YIKT6szK1bt+jatSsFChRgzZo16HQ62rVrx7lz5/D39ydDhgxaRxSx/vgjfr+MhQvVX5FCWBoPD/jmG3U+caK6jCjeKlWqVIwfP57z58/TuXNn7OzsCAgIiBuTd+fOHa0jJispPqzE48ePGTJkCLly5eKHH34gJiaG//3vfwQFBbF06VKyyQBG8/L4MbRvr8579IDmzbXNI8T7aNUKKlWC8HAYNEjrNBblww8/ZMGCBRw7dowGDRoQExMTNxvRz8+PZ8+eaR0xWUjxYeGeP3/OlClTyJkzJ5MnT+b58+dUqFCBwMBAfv/9d4oUKaJ1RJGQcePgwQMoUACmTtU6jRDvR6dT+77odGo7gMBArRNZnIIFC7J+/Xp27NhBmTJlCAsLY/To0eTKlYvvvvsOvV6vdUSTkuLDQkVHR7NgwQJy587N4MGDefz4MQULFuS3335j165dVK5cWeuI4nUuXlRrI4C6Ru7iom0eIUyheHHo0kWd9+6tpuCKJKtSpQr79u1jzZo15MqVi3v37tGjRw8KFizIL7/8YjUzY6T4sDBGo5FffvmFQoUK8cUXX3Dz5k2yZMnCkiVLCA4OpmHDhrJWh7kbMkTNcqlTBz76SOs0QpjOuHFqkbzgYJg/X+s0Fkun09G8eXNOnTrFt99+S/r06Tl//jzNmzenQoUK7Ny5U+uI702KDwvyzz//ULZsWZo3b87Zs2dJmzYt06dP5+zZs3To0EHW6rAEu3fD2rVqe3K53CKsTbp0MGaMOh8+XE0hF+/M0dGRHj16cOHCBUaOHImbmxv79u2jSpUqNGrUiNOnT2sd8Z1J8WEBjh49St26dalZsyYHDx7E3d2dkSNHcunSJfr164eLdNtbBoNBbRYHalvywoW1zSNEcujeHQoWVIXH119rncYqeHl5MWbMGC5cuEDXrl2xt7dn/fr1FCpUiC5dunDr1i2tIyaZFB9m7MKFC3z66aeUKFGCLVu24OjoSM+ePbl48SJjxozBy8tL64giKX76CQ4cAHd3GDtW6zRCJA8HBzX4FGDuXDh+XNs8ViRTpkzMmzePEydO0LhxYwwGA/PnzydXrlyMGDGCkJAQrSMmmhQfZujx48f06tWL/Pnzs3r1agBat27NmTNnmD17Nt7e3honFEkWEaEWYwI15iNjRm3zCJGcatRQ08cNBjX41EoGSZqLfPnyERAQwK5duyhfvjzPnz9n/Pjx5MyZk9mzZxMVFaV1xLeS4sNM3L9/n4CAAPr27Uu3bt3iNh2qV68eR48eZcWKFeSQRags18yZcPWqWgky9tKLENZs6lQ1k2v7djXOSZhcxYoV2b17N7/++it58+blwYMH9O7dmwIFCvDzzz+b9cwYKT40cv36dVasWEG3bt0oUKAAGTJkoGnTpnz33XdERkZSpkwZtm/fzqZNmyhWrJjWccX7uH8fJkxQ5xMmgJubtnmESAlZs8Lgwep8wAC1AJkwOZ1OR5MmTThx4gTz5s3D29ubixcv0rJlS8qWLcv27du1jpggB60D2AKj0ci5c+fYuXMngYGBBAYGcvXq1VeeV6BAASpVqkSGDBkYOXIkTk5OGqQVJufnByEhUKIEtG2rdRohUs6gQbB4MVy7pjZN9PPTOpHVcnBwoGvXrrRp04bp06czZcoUDh48SPXq1fn444+ZNGkShQoV0jpmHOn5SAYxMTEcPXqUWbNm0bx5czJmzEi+fPn44osvWLZsGVevXsXe3p5SpUrRv39/AgICuH//PidPnuTbb7+lVKlSslaHtTh9Gr7/Xp1Pm6am2AphK9zc4jebmzxZbT4nkpWHhwdff/01Fy9e5Msvv8TBwYGNGzdStGhROnXqxI0bN7SOCEjPh0lERkZy6NChuJ6N3bt3vzLq2NnZmbJly1KlShUqV65M+fLlZWdZWzBokFrpsWFDqFZN6zRCpLxmzdR7f/t2+OorGf+RQry9vZkzZw59+/Zl2LBhrF27lsWLF7Nq1Sp69eql+eV8KT7eQWhoKHv37o0rNvbv309ERMRLz/H09KRixYpUrlyZKlWqULp0aZxlu3Tb8s8/sGGDmno4ZYrWaYTQRuy+L8WKwS+/qP8XNWponcpm5M6dmzVr1rBv3z4GDRrEzp07+eabb/D09KREiRLky5dPk1xSfCTCo0eP2LVrF4GBgezcuZPDhw8T8599C9KlSxfXq1GlShWKFCmCg4M0r82KiVGD7AC6dYO8ebXNI4SWCheGL7+Eb79VU2+DglRRLlJMuXLl2LFjBxs2bGDQoEEYjUZNZ1DKv34Cbt68GdersXPnTk6cOPHKc7JkyfJSsZE3b14ZpyHiLVumfsCmSgWjRmmdRgjtjR4Nq1bByZNq8bFevbROZHN0Oh0NGjSgVq1arF69WtPfWUkuPgIDA/nmm284fPgwt2/fJiAggMaNGyf43Nj1KmbMmEHfvn3fM2ryMBqNXLhw4aVi49KlS688L1++fHGFRuXKlcmaNasGaYVFCAtT+1qA+pgunbZ5hDAHadKojee6d1fLrrdqBenTa53KJjk4OJAmTRptMyT1E8LCwuJGzTZt2vS1zwsICGDfvn34+Pi8V0BTMxgMnDhxIq7QCAwM5M6dOy89x87OjmLFisUVG7HTX4VIlGnT4NYtyJZN/roT4kVffAHz5qldb0eMiJ8JJmxOkouPevXqUa9evTc+5+bNm/Tq1YvNmzfz8ccfv3M4U7p16xZdu3Zl165dPHny5KXHnJycKFOmTFyxUaFCBdk3RbybW7fUlEKASZPUCo9CCMXeHmbPhipVYP58NR6qeHGtUwkNmHzMh8FgoF27dgwcOJCCBQu+9fmRkZFERkbG3Y6doqrX69Hr9SbL5eXlxdatW4mMjMTd3Z3y5ctTqVIlKleuTKlSpXB1dX3p+ab82kkR+3W1+vrWQqt2tB8+HLvwcAxlyxLTpAlY+L+jvB9NQ9rxBeXKYd+yJXY//YShZ09itm1TM2ISQdrRNJKrHZPyeiYvPiZPnoyDgwO9e/dO1PMnTpzI6NGjX7l/y5YtuJl4GeqePXuSMWNGcuTIgb29PQDPnj1j27ZtJv06prB161atI1iFlGxHr8uXqfbjjwDsatKEx3/8kWJfO7nJ+9E0pB0Vl9q1qbluHQ579nBkyBBuVq2apM+XdjQNU7djeBKW0Ddp8XH48GFmzpzJkSNHEj2KdujQofR/YaOtkJAQfH19qVOnjskvfdSvX9+kr5cc9Ho9W7dupXbt2jg6Omodx2KleDsajdjXr4/OaMTQvDnlrWTzOHk/moa046t0N2/CqFGU/Plnio4cCR4eb/0caUfTSK52/O/imm9i0uJj586d3Lt3jyxZssTdFxMTw4ABA/D39+dKAkvrOjs7J7j4lqOjo02/uWz9+zeVFGvHTZvg77/ByQm7KVOws7J/O3k/moa04wsGDYIlS9Bdvozj1KkwfnyiP1Xa0TRM3Y5JeS2TbjTRrl07jh07RlBQUNzh4+PDwIED2bx5sym/lBDmIzpaLRsNagGl7Nm1zSOEJXBxgRkz1PnUqXDxorZ5RIpKcs9HaGgoFy5ciLt9+fJlgoKCSJMmDVmyZCFt2rQvPd/R0ZGMGTOSV1Z4FNZqwQK1gVzatPHrewgh3q5hQ6hdG7Zuhf794bfftE4kUkiSez4OHTpE8eLFKf7v9Kj+/ftTvHhxvv76a5OHE8LshYSoBZNArWSaOrWmcYSwKDodzJypllpfvx6kh9xmJLnno1q1ahiNxkQ/P6FxHkJYjUmT4P59yJNHrVkghEia/PnVYnwzZkCfPnDsGDg5aZ1KJDOTjvkQwqZcuxZ/zXrKFJABcEK8m1GjIEMGOHtWbT4nrJ4UH0K8q2HDICICqlZV166FEO8mVSqYMEGdjx4Nd+9qm0ckOyk+hHgXBw/CihXqfNq0RK/QKIR4jY4doVQpNY5q2DCt04hkJsWHEEllNKqR+QDt2kHJktrmEcIa2NnBrFnqfNEiOHBA2zwiWUnxIURSBQTArl1qnYIkLIwkhHiL8uWhfXt13rs3GAza5hHJRooPIZIiKgoGD1bnX30Fvr7a5hHC2kyapJZa378fli3TOo1IJlJ8CJEU330HFy6At7daHloIYVqZMsHIkep8yBA1BkRYHSk+hEisR49gzBh1PnYseHpqm0cIa9WnD+TODXfuwLhxWqcRyUCKDyESa9w4ePwYChWCTp20TiOE9XJ2Bn9/de7vD+fOaZlGJAMpPoRIjAsX4hc/mjoV7O21zSOEtatfXx16PfTrp3UaYWJSfAiRGEOGqB+CdeuqQwiR/GbMUCsHb9oEGzdqnUaYkBQfQrzNrl3wyy9qHYKpU7VOI4TtyJMnvtejb1+IjNQ0jjAdKT6EeBODAQYMUOedO6vxHkKIlDNiBGTMCBcuYBe7CJmweFJ8CPEmP/2kVlr08Iif6SKESDmenjB5MgB2Eyfi8uiRxoGEKUjxIcTrRETA0KHqfPBg9deXECLltW0L5cqhCw2lwI8/ap1GmIAUH0K8zsyZcPUqfPhh/F4uQoiUZ2cHs2cD4LtjB5w8qXEg8b6k+BAiIffuxe/bMmECuLlpm0cIW1eqFIbGjQGwnzJF2yzivUnxIURC/Pzg2TMoUUJ1+QohNBfz72VQ3U8/qbV3hMWS4kOI/zp9Gn74QZ1Pn666fIUQ2itenDslS6IzGNQGdMJiyU9VIf5r4ECIiYFGjaBqVa3TCCFecK5FC3Xy449w7Zq2YcQ7k+JDiBf9/bdaSdHBAeS6shBm53G+fBiqV4foaPk/asGk+BAiVkxM/IJi3bur1RWFEGbHEDsFfsECuH1b2zDinUjxIUSspUshOBhSpYKvv9Y6jRDiNYxVq0KFCmq59WnTtI4j3oEUH0IAhIXB8OHqfMQISJdO2zxCiNfT6dT/U4C5c+HBA23ziCST4kMIUBvG3b4N2bNDr15apxFCvM1HH6mp8OHh4O+vdRqRRFJ8CHHrVvzAtUmTwNlZ2zxCiLd7sfdj9mx48kTTOCJppPgQYuRI9ddT+fIQO41PCGH+GjWCggUhJAS+/VbrNCIJpPgQti04GBYvVufTpqm/poQQlsHOLn6s1owZEBqqbR6RaFJ8CNtlNKqptUYjfPKJ6vkQQliWTz6B3Lnh0SOYN0/rNCKRpPgQtmvTJrWomJOTLNUshKWyt4fYdT+mToXnz7XNIxJFig9hm6Kj1TLqAH36qFkuQgjL1LYtZMkCd+/CwoVapxGJIMWHsE3z56sN5NKmhWHDtE4jhHgfjo4wZIg6nzwZoqK0zSPeSooPYXtCQmDUKHXu5wepU2uZRghhCh07QqZMcOOGWq1YmDUpPoTtmTgR7t+HvHmha1et0wghTMHFJf5S6sSJ6tKqMFtSfAjbcvWqmpIHamExR0dt8wghTKdLF7U1wqVLsHq11mnEG0jxIWzLsGFqM6pq1aBBA63TCCFMyd0d+vdX5+PHg8GgbR7xWlJ8CNtx4ACsXKkWEpMFxYSwTj16qHFcZ87Ar79qnUa8hhQfwjbELigG0K6d2pBKCGF9vLygd291Pm6c+r8vzI4UH8I2BATArl3g6qq6Y4UQ1qt3b/DwUNsnbNyodRqRACk+hPWLioLBg9X5gAGQObO2eYQQySttWvjyS3UuvR9mSYoPYf2++w4uXABvbxg0SOs0QoiU0L+/mn67f7/aRkGYFSk+hHV79AjGjFHnY8eCp6e2eYQQKcPbW029BdX7IcyKFB/CqtlNmACPH0PhwtCpk9ZxhBApaeBAtZbPjh2wc6fWacQLpPgQVsv99m3s5s5VN6ZOVbtfCiFsR+bMatl1kIHmZkaKD2G1Cvz4Izq9Hj76COrU0TqOEEILgwerPzw2b4aDB7VOI/4lxYewSrpdu/DZtw+jnZ3q9RBC2KYcOaBNG3UuvR9mQ4oPYX2MRuz+ndVi6NQJChbUOJAQQlNDh6oVjX/7DY4f1zqNQIoPYY3WrsXu0CGiXVwwjBqldRohhNby5YMWLdT5hAnaZhGAFB/C2uj1avM44ELjxmq6nRBCDB+uPv70E5w9q20WIcWHsDILF8KFCxjTp+dio0ZapxFCmIsiRaBhQ7Xa6aRJWqexeVJ8COsRFgajRwNgGD6caFdXjQMJIcxKbO/HsmVw5YqmUWydFB/Cevj7w507kCMHhs8/1zqNEMLclCmjpt3HxMDkyVqnsWlSfAjr8OABTJmizseOBScnbfMIIczTiBHq46JFcPOmtllsmBQfwjpMmAAhIVCsGLRqpXUaIYS5qlwZqlRRu13LGkCakeJDWL6rV2HOHHU+aRLYydtaCPEGsb0f338P9+5pm8VGJfmndGBgIA0aNMDHxwedTse6deviHtPr9QwePJjChQvj7u6Oj48P7du359atW6bMLMTLRo1Sf8VUry7LqAsh3q5WLShdGp4/hxkztE5jk5JcfISFhVG0aFHmxP6l+YLw8HCOHDnCyJEjOXLkCL/++itnz56lYcOGJgkrxCuOH4elS9X55MlqFUMhhHgTnS6+9+Pbb+HRI23z2CCHpH5CvXr1qFevXoKPpUqViq1bt75037fffkuZMmW4du0aWbJkebeUQrzOsGFq3n7z5uovGSGESIz//U+t/XHsGMyerXpQRYpJcvGRVE+fPkWn05E6deoEH4+MjCQyMjLudkhICKAu4ej1+uSOZ3Ziv2db/N6TSrdrFw4bNmC0tyfaz0+tbvovaUfTkHY0DWlH0zB1O+oGD8ahTRuMM2cS3bMneHmZ5HXNXXK9H5Pyejqj0Wh81y+k0+kICAigcePGCT4eERFBxYoVyZcvHytWrEjwOX5+foz+d2GoF61cuRI3N7d3jSasndFI5SFDSHP2LJfr1uVY9+5aJxJCWJqYGGr07o3nzZucbN+eC02bap3IooWHh9O6dWuePn2K11sKuWQrPvR6Pc2aNePGjRts3779tUES6vnw9fXlwYMHbw1vjfR6PVu3bqV27do4OjpqHcds6davx6F5c4yurkSfPg0+Pi89Lu1oGtKOpiHtaBrJ0Y66Zctw6NwZY/r0RJ8/DzbwR29yvR9DQkJIly5dooqPZLnsotfr+eSTT7h69Sr//PPPG0M4Ozvj7Oz8yv2Ojo42/Z/U1r//N4qOhpEjAdD164dj1qyvfaq0o2lIO5qGtKNpmLQd27WDcePQXb6M45Il0KePaV7XApj6/ZiU1zL5ggixhcf58+f566+/SJs2ram/hLB1S5fC6dOQJg0MGqR1GiGEJXN0hCFD1PmUKfBCT7xIPkkuPkJDQwkKCiIoKAiAy5cvExQUxLVr19Dr9TRv3pxDhw6xYsUKYmJiuHPnDnfu3CEqKsrU2YUtev48flT6sGGQKpW2eYQQlq9DB/jwQ7h1C5Ys0TqNTUhy8XHo0CGKFy9O8eLFAejfvz/Fixfn66+/5ubNm6xfv54bN25QrFgxMmXKFHfs2bPH5OGFDfr2W7hxA3x9oUcPrdMIIayBs3N8L+qkSS/NnBPJI8ljPqpVq8abxqi+x/hVId7syROYOFGdjxkDLi6axhFCWJHPP4fx4+HKFVi5UvWGiGQjm2AIyzF5Mjx+DAULqkFiQghhKm5uMGCAOp8wAWJitM1j5aT4EJbh5k3w91fnEyeCvb2mcYQQVqh7d/jgAzh3Dtau1TqNVZPiQ1iG0aMhIgIqVlTLIgshhKl5ekLfvup83DgwGDSNY82k+BDm78wZWLRIncvmcUKI5NSrlypCTpyA33/XOo3VkuJDmL/hw9X114YNVc+HEEIklw8+gJ491fm4cWrjSmFyUnwI87ZvH/z6K9jZqUFgQgiR3Pr1A1dXOHQItmzROo1VkuJDmC+jMX7lwQ4d1CwXIYRIbunTQ7du6nzsWOn9SAZSfAjz9eefsGOHWgAogZ2PhRAi2Xz1FTg5we7dEBiodRqrI8WHME8GQ3yvR8+eakVTIYRIKT4+0LmzOh83TtssVkiKD2GeVq6EY8fU3i1Dh2qdRghhiwYNAgcH+OsvNf5MmIwUH8L8REbCyJHqfPBgkJ2RhRBayJYtfjXl8eM1jWJtpPgQ5uf779X+CpkyQZ8+WqcRQtiyIUPUbLsNG+DoUa3TWA0pPoR5CQlRo8sB/PzUfgtCCKGVPHmgZUt1LtP9TUaKD2Fepk2DBw/Uf/hOnbROI4QQMGyY+vjLL3DqlLZZrIQUH8J83L2rig9Qf2E4OGibRwghAAoVgiZN1HofEydqncYqSPEhzMfYsRAWBmXKQNOmWqcRQoh4w4erjytXwsWL2maxAlJ8CPNw8aIaaAowaZJsHieEMC8lS0K9emoNokmTtE5j8aT4EOZh5EiIjoaPPoLq1bVOI4QQrxoxQn388Ue4dk3bLBZOig+hvSNHYNUqdS7XU4UQ5qpCBfXHkV4P33yjdRqLJsWH0F7sCqatW0OxYppGEUKIN4rt/Zg/H+7c0TaLBZPiQ2jr77/VltWOjvHrewghhLmqXh3Kl1crMcfOzhNJJsWH0I7RGL95XLdukCOHtnmEEOJtdLr43o+5c9W6RCLJpPgQ2lm7Fg4dAg+P+P/MQghh7urVg+LF1dIAM2dqncYiSfEhtKHXx68a+NVXkCGDtnmEECKxXuz9mDULnjzRNI4lkuJDaGPhQrhwAdKnh/79tU4jhBBJ07gxFCig9qOaM0frNBZHig+R8sLCYPRodf711+DpqW0eIYRIKju7+FVPZ8yA0FBt81gYKT5EyvP3V1PUcuSALl20TiOEEO/mk08gVy54+DB+hWaRKFJ8iJT18CFMmaLOx44FJydt8wghxLtycIhfp2jqVHj+XNs8FkSKD5GyJkxQ10iLFYNWrbROI4QQ76dtW8iSRfXmLlqkdRqLIcWHSDlXr8K336rzSZPUNVMhhLBkTk4weLA6nzwZoqK0zWMh5Ke/SDmjRqn/mNWrQ506WqcRQgjT6NQJMmaE69dh2TKt01gEKT5Eyjh+HJYuVeeTJ6t58kIIYQ1cXNR6RaA2x4yO1jaPBZDiQ6SMYcPUcurNm0Pp0lqnEUII0+raFdKmhYsX4aeftE5j9qT4EMlv507YsAHs7WH8eK3TCCGE6Xl4QL9+6nzcOBn78RZSfIjkZTTGD8b6/HPIk0fbPEIIkVx69oR06eDMGTWoXryWFB8iea1fD3v3gqurWs1UCCGsVapUMHu2Oh83To11EwmS4kMkn+jo+M3j+vUDHx9t8wghRHJr2RIaNVKbZ3bqJINPX0OKD5F8li6FU6cgTRoYNEjrNEIIkfx0OvjuO0idGg4dgunTtU5klqT4EMnj+XO1rgeo3o9UqbTNI4QQKcXHR202B+py89mz2uYxQ1J8iOQxZw7cuAG+vtCjh9ZphBAiZXXoAHXrQmSkuvwSE6N1IrMixYcwvSdP1B4uAGPGqAV4hBDCluh0aqdbDw/Ys0f9QSbiSPEhTG/yZHj8GAoWhHbttE4jhBDayJo1fhfvoUPh0iVt85gRKT6Ead28Cf7+6nziRLWwmBBC2KquXaFqVQgPV2sdGY1aJzILUnwI0xo9GiIioGJF+N//tE4jhBDasrODBQvUWkfbtsH8+VonMgtSfAjTOXMGFi1S57J5nBBCKLlyxW8t8dVXavdbGyfFhzCd4cPViO6GDVXPhxBCCKV3byhXDp49g27dbP7yixQfwjT274dff1VdjLEzXYQQQij29qpn2MkJNm2C5cu1TqQpKT7E+3tx87gOHdQsFyGEEC/Lnx/8/NR5nz5w546mcbQkxYd4f3/+CTt2gLOzGnAqhBAiYV99BSVKqOUIvvzSZi+/SPEh3o/BoOavg9pO2tdX2zxCCGHOHB3V5RcHBwgIgLVrtU6kCSk+xPtZtQqCg9XeLbFFiBBCiNcrWjR+x+8ePeDBA23zaECKD/HuIiNhxAh1PngwpE2rbR4hhLAUw4dDoUJw/74a/2FjpPgQ7+777+HKFciUySb/8wghxDtzclKXX+zsYOVKWL9e60QpSooP8W5CQmDsWHXu5wdubprGEUIIi1O6tBqACmrtjydPNI2TkqT4EO9m2jR1nTJPHrVdtBBCiKTz81M/R2/fhgEDtE6TYpJcfAQGBtKgQQN8fHzQ6XSsW7fupceNRiNff/01mTJlwtXVlVq1anH+/HlT5RXm4O5dVXyAWlDMwUHbPEIIYalcXdXlF51OfdyyRetEKSLJxUdYWBhFixZlzpw5CT4+ZcoUZs2axbx589i/fz/u7u7UrVuXiIiI9w4rzMTYsRAWBmXKQNOmWqcRQgjLVrEi9Oqlzr/4Qi3BbuWSXHzUq1ePcePG0aRJk1ceMxqN+Pv7M2LECBo1akSRIkVYunQpt27deqWHRFioHTtg3jx1PmmSbB4nhBCmMGECZM8O167BkCFap0l2Jh3zcfnyZe7cuUOtWrXi7kuVKhVly5Zl7969pvxSQgvXr0OLFmrzuLZtoXp1rRMJIYR1cHeH+fPV+XffQWCgtnmSmUkv1t/5d516b2/vl+739vaOe+y/IiMjiYyMjLsdEhICgF6vR6/XmzKeRYj9ns3ue4+IwL5JE+zu38dYtCjR334L5pbxBWbbjhZG2tE0pB1Nw+rbsUoV7Dt3xm7hQoydOxN96FCyzCRMrnZMyutpPlJw4sSJjE5gP5AtW7bgZsPTN7du3ap1hHhGI8VnzybL4cNEeXqy/csveb59u9apEsWs2tGCSTuahrSjaVhzOzrUqEGNdetwvXCBqx06cLJjx2T7WqZux/Dw8EQ/16TFR8aMGQG4e/cumTJlirv/7t27FCtWLMHPGTp0KP3794+7HRISgq+vL3Xq1MHLy8uU8SyCXq9n69at1K5dG0dHR63jAGA3dy72//yD0c4Ou59/pnrNmlpHeitzbEdLJO1oGtKOpmEr7ahzd4fGjcn5++9k++orjGXLmvT1k6sdY69cJIZJi4/s2bOTMWNG/v7777hiIyQkhP3799O9e/cEP8fZ2RlnZ+dX7nd0dLTqN9fbmM33v3Nn3Nxz3eTJOHz0kcaBksZs2tHCSTuahrSjaVh9OzZqBO3aoVu2DIcuXeDoUbVruImZuh2T8lpJHnAaGhpKUFAQQUFBgBpkGhQUxLVr19DpdPTt25dx48axfv16jh8/Tvv27fHx8aFx48ZJ/VJCazduQPPmEB0NLVva1AI4QgihKX9/8PaG06fjV5O2Iknu+Th06BDVX5jlEHvJpEOHDixZsoRBgwYRFhZGly5dePLkCZUqVeLPP//ExcXFdKlF8ouMhGbN4N49KFIEFi6UabUWwmAwEBUVZZLX0uv1ODg4EBERQUxMjEle0xI4OTlhZycLQAsNpUmjZr00a6aWNWjWDIoX1zqVySS5+KhWrRpGo/G1j+t0OsaMGcOYMWPeK5jQkNGotnk+cAA++AACAtQ0MGH2oqKiuHz5MgaDwSSvZzQayZgxI9evX0dnQ8WnnZ0d2bNnx8nJSesowpY1baqWN1izBjp2hIMHwUouN2k+20WYoR9+UD0ddnawejXkyKF1IpEIRqOR27dvY29vj6+vr0n+cjcYDISGhuLh4WEzPQEGg4Fbt25x+/ZtsmTJYlNFlzBDs2fDP/9AcDBMngwjRmidyCSk+BAv27MnfpnfCROgTh1t84hEi46OJjw8HB8fH5NNU4+9hOPi4mIzxQdA+vTpuXXrFtHR0dY9sFGYP29vmDUL2rSBMWOgSRMoWFDrVO/Ndn6aiLe7dUtdV9Tr1UDTQYO0TiSSIHZMhlwqeH+xbWhL41yEGfv0U2jQQP1s7thRTQKwcFJ8CCUqShUcd+6oqnrxYhlgaqHkMsH7kzYUZkWng7lzIVUqNe7D31/rRO9Nig+h9OkDe/dC6tSwbh14eGidSAghRKwPP4Tp09X5yJFw7py2ed6TFB8CFixQO9XqdLBiBeTKpXUiIYQQ/9WxI9SuDRER0LkzmGhWmxak+LB1+/erabWgFrKpX1/bPMKm6HS6Nx5+fn4ABAQEUK5cOVKlSoWnpycFCxakb9++mmYXIsXpdGrnWw8P2LVLXYqxUFJ82LI7d9QA06goNYJ66FCtEwkbc/v27bjD398fLy+vl+776quv+Pvvv2nZsiXNmjXjwIEDHD58mPHjx1vvzqZCvEnWrGrKLcDgwXDliqZx3pVMtbVVUVFq8ZqbNyF/fvjxR7WuhxApKHYzSoBUqVKh0+leug/g999/p2LFigwcODDuvjx58siWDcJ2desGP/0EgYHwxRewZYvFTRCQ4sNWDRiguu28vNQKpp6eWicSJmY0GpO0xXVCDAYDYWFh2NvbJ2mdDzc3N5PNGMmYMSMrV67kxIkTFCpUyCSvKYRFs7NTC0EWKQJ//QWLFqkxIBZEig9btGQJfPutOl++HPLm1TSOSB7h4eF4aDRrKTQ0FHcTLcnfq1cvdu7cSeHChcmaNSvlypWjTp06tGnTJsEdsYWwCblyqXF6X30F/fvDRx+pGTEWQvrZbc2hQ6rLDsDPTy1cI4QZc3d3Z+PGjVy4cIERI0bg4eHBgAEDKFOmzHv37Ahh0fr2hTJlICRE/Vx/w75r5kZ6PmzJvXtqo6LISFV0jBypdSKRjNzc3AgNDX2v1zAYDISEhODl5ZXkyy6mljNnTnLmzMnnn3/O8OHDyZMnDz/99BMdO3Y0+dcSwiLY26tLLiVKwIYNsHKlWobdAkjxYSv0evjkE7h+HfLkgWXLZICpldPpdO996cNgMBATE4O7u7tZ7e2SLVs23NzcCAsL0zqKENoqWBC+/lptONe7N9SqpfaDMXNSfNiKgQNhxw41sHTdOrVMrxAWwM/Pj/DwcOrXr0/WrFl58uQJs2bNQq/XU7t2ba3jCaG9QYNg7VoICoKePWHNGq0TvZX5/Ckjks+yZTBzpjpfulRNrRXCQlStWpVLly7Rvn178uXLR7169bhz5w5btmwhrwyWFgIcHdV+XA4OqghZu1brRG8lPR/W7sgR6NJFnY8cCbI2gjBTn332GZ999tkr91evXp3q1aunfCAhLEmxYjBkCIwbp1atrl4d0qbVOtVrSc+HNXvwQK1cGhGhlk3/d6lqIYQQVmjECChQQE0uMPPtB6T4sFbR0dCyJVy7puaDr1ghA0yFEMKaOTur2S92dmoNpw0btE70WvLbyFoNGQL//APu7mqAaerUWicSQgiR3MqWVYuOgVr74+lTbfO8hhQf1mjVKpg2TZ3/+KOaiiWEEMI2jBkDuXOrvbte2BPJnEjxYW2Cg+PX+B86VO1aK4QQwna4uqq9XwDmz1f7v5gZKT6sycOHaoDp8+dQt65a918IIYTtqVxZrfkBaufb91zt2NSk+LAWMTHw6adw+TLkyKGW2bW31zqVEEIIrUycCFmzwpUrMGyY1mleIsWHtRg+HLZuBTc3CAiANGm0TiSEEEJLHh6wYIE6nz0bdu7UNs8LpPiwBj//DJMnq/NFi6BIEW3zCCGEMA+1asHnn6vzzp3VZXkzIMWHpTt+HGJ39Rw4UK3tIYQQQsSaOhU+/BDOn4dRo7ROA0jxYdkeP1YDTMPDVXU7YYLWiYRIss8++4zGr1n2Pzg4mIYNG5IhQwZcXFzIli0bLVu25N69e688d+LEidjb2/PNN9/E3ZctWzZ0Ot1rj4SWcxfC6qRKBfPmqfNp09AdPKhtHqT4sFwxMdCmDVy8CNmywerValMhIazE/fv3qVmzJmnSpGHz5s2cPn2axYsX4+PjQ1hY2CvPX7RoEYMGDWLRokVx9x08eJDbt29z+/ZtfvnlFwDOnj0bd9/M2A0XhbB2//sftG0LBgP2X3yBnV6vaRz5bWWpRo2CP/5Q87kDAsx6AyEh3sXu3bt5+vQpCxYswOHfwjp79uwJbjK3Y8cOnj9/zpgxY1i6dCl79uyhQoUKpE+fPu45af4dhJ0hQwZSy4q/whb5+8OWLehOnSLPmjXQqJFmUaTnwxL9+iuMH6/OFyxQuxkK8V9GI4SFaXMYje8dP2PGjERHRxMQEIDxLa+3cOFCPv30UxwdHfn0009ZGLvAkhAiXtq0MGcOALl/+QXOndMsivR8WJpTp6BDB3Xerx+0bq1tHmG+wsPVVLv3YAekfpdPDA1V+wq9h3LlyjFs2DBat25Nt27dKFOmDDVq1KB9+/Z4e3vHPS8kJIS1a9eyd+9eANq2bUvlypWZOXMmHu/5/QthdZo3x9CxI8EeHhTKnVuzGNLzYUmePIHGjdUP9ho1YMoUrRMJkazGjx/PnTt3mDdvHgULFmTevHnky5eP48ePxz1n1apV5MyZk6JFiwJQrFgxsmbNyk8//aRVbCHMWsz333OtZk3Q6TTLID0flsJgUIOFzp+HLFlkgKl4Oze3915S2WAwEBISgpeXF3Z2Sfhbxc3tvb7ui9KmTUuLFi1o0aIFEyZMoHjx4kydOpUff/wRUJdcTp48GTcuJDb3okWL6By7z5EQwqzIby9LMXo0bNwILi5qgOkLA+mESJBO996XPjAY1Mwqd3dISvGRTJycnMiZM2fcbJfjx49z6NAhtm/fHjegFODRo0dUq1aNM2fOkC9fPq3iCiFeQ4oPS/Dbb2qLZIAffoASJbTNI4SJPX36lKCgoJfuO378OJs3b6ZVq1bkyZMHo9HI77//zqZNm1i8eDGgej3KlClDlSpVXnnN0qVLs3DhwpfW/RBCmAcpPszdmTPQrp067907/lwIK7J9+3aKFy/+0n3Vq1cnV65cDBgwgOvXr+Ps7Ezu3LlZsGAB7dq1IyoqiuXLlzN48OAEX7NZs2ZMmzaNCRMm4OjomBLfhhAikaT4MGchIWqA6bNnUKWKWiJXCCuzZMkSlixZkuTPc3Jy4sGDB699fNCgQQwaNCjudrVq1d46ZVcIkTK0v4grEmYwQPv2cPYsZM6sNo+Tv96EEEJYASk+zJTdxIlqrIezs1pU7IV1DYQQQghLJpddzJD3wYPYxW4SN3culC6tbSAhhBDChKTnw9ycO0fJGTPQGY3w5ZfQsaPWiYQQQgiTkp4Pc2AwwIEDsHYtDitWoAsPx1ChAnYzZmidTAghhDA5KT60EhMDe/bA2rVqTMeNGwDogDBvb5xWr8bOyUnbjEIIIUQykOIjJUVHw86d8QXHnTvxj3l6QoMGRDduzD9GIx9lzKhdTiGEECIZSfGR3PR62LZNFRwBAfDiugSpU0OjRtCsGdSuDS4uGPV6DJs2aRZXCCGESG5SfCSHyEj46y/45RdYtw4eP45/LE0aaNIEmjdXO9PKpRUhhBA2RooPU3n+HLZsUT0c69er1UljZcgATZuqHo6qVWWxMCGEEDZNptq+j7AwVWy0aqUKjMaNYflyVXhkygQ9e8L27XDrllqvo1YtKTyESMD9+/fp3r07WbJkwdnZmYwZM1K3bl12794NQHBwMA0bNiRDhgy4uLiQLVs2WrZsyb179/Dz80On073xEEKYF+n5SKpnz9TW9mvXwqZNqscjlq+vupzSrBmUL28WW5ALYQmaNWtGVFQUP/74Izly5ODu3bv8/fffPHz4kPv371OzZk3+97//sXnzZlKnTs2VK1dYv349YWFhfPXVV3Tr1i3utUqXLk2XLl344osvNPyOhBBvIsVHYjx5Ar//rgqOzZvVmI5Y2bOrgqN5c7USqfyVJUSSPHnyhJ07d7J9+3aqVq0KQNasWSlTpgwA69at4+nTpyxYsAAHB/UjK3v27FSvXj3uNTw8POLO7e3t8fT0JKPMGBPCbEnx8TqPHqm9Vdauha1b1ayVWLlzQ4sWquAoVkwKDmGWjEYID3+/1zAY1NVFe/ukdeS5uSX+v4WHhwceHh6sW7eOcuXK4ezs/NLjGTNmJDo6moCAAJo3by6XUYSwAlJ8vOjePTU7Ze1aNT02Ojr+sQIF4ns4ChWSgkOYvfBweKFD4B3ZAamT/FmhoeDunrjnOjg4sGTJEr744gvmzZtHiRIlqFq1Kq1ataJIkSKUK1eOYcOG0bp1a7p160aZMmWoUaMG7du3x1s2XBTCIsmghNu34bvv1LTXTJmga1fV0xEdDUWLwtixcOoUnDwJo0dD4cJSeAhhYs2aNePWrVusX7+ejz76iO3bt1OiRAmWLFkCwPjx47lz5w7z5s2jYMGCzJs3j3z58nH8+HFtgwsh3olt9nxcv65WGF27FnbvVv3TsUqWjB80mju3dhmFeE9ubqoH4n0YDAZCQkLw8vLCLgnXXdzckv61XFxcqF27NrVr12bkyJF8/vnnjBo1is8++wyAtGnT0qJFC1q0aMGECRMoXrw4U6dO5ccff0z6FxNCaMp2io/Hj2HRIlVw7Nv38mPlyqmCo2lTNYBUCCug0yX+0sfrGAxqGyJ395SfvFWgQAHWrVuX4GNOTk7kzJmTsLCwlA0lhDAJkxcfMTEx+Pn5sXz5cu7cuYOPjw+fffYZI0aM0HagWFQUDBqkfprqdFCpkurdaNpUTZEVQmji4cOHtGjRgk6dOlGkSBE8PT05dOgQU6ZMoVGjRmzYsIHVq1fTqlUr8uTJg9Fo5Pfff2fTpk0sXrxY6/hCiHdg8uJj8uTJzJ07lx9//JGCBQty6NAhOnbsSKpUqejdu7epv1zieXtD376QM6da3jxTJu2yCCHieHh4ULZsWWbMmMHFixfR6/X4+vryxRdfMGzYMG7fvo2bmxsDBgzg+vXrODs7kzt3bhYsWEC7du20ji+EeAcmLz727NlDo0aN+PjjjwHIli0bq1at4sCBA6b+Ukk3bZrWCYQQ/+Hs7MzEiROZOHFigo/nyJGDH374IdGvd+XKFRMlE0IkF5MXHxUqVOCHH37g3Llz5MmTh+DgYHbt2sX06dMTfH5kZCSRLyzaFfLvnih6vR79i2tr2IjY79kWv3dTssV21Ov1GI1GDAYDBoPBJK9p/Hcwduzr2gqDwYDRaESv12Nvb//er2eL78fkIO1oGsnVjkl5PZ3R+OJUj/dnMBgYNmwYU6ZMwd7enpiYGMaPH8/QoUMTfL6fnx+jR49+5f6VK1fi9i5D5oWwUQ4ODmTMmBFfX1+cZLfk9xIVFcX169e5c+cO0S+u9yOEeK3w8HBat27N06dP8fLyeuNzTV58rF69moEDB/LNN99QsGBBgoKC6Nu3L9OnT6dDhw6vPD+hng9fX18ePHjw1vDWSK/Xs3XrVmrXro2jbEL3zmyxHSMiIrh+/TrZsmXDxcXFJK9pNBp59uwZnp6eNrWyaEREBFeuXMHX19ckbWmL78fkIO1oGsnVjiEhIaRLly5RxYfJL7sMHDiQIUOG0KpVKwAKFy7M1atXmThxYoLFh7Oz8yvLKQM4Ojra9JvL1r9/U7GldoyJiUGn02FnZ5ekNTneJPZSS+zr2go7Ozt0Op3J3z+29H5MTtKOppEc7+/EMvlPk/Dw8Fd+SNnb29vU9WIhhBBCvJ7Jez4aNGjA+PHjyZIlCwULFuTo0aNMnz6dTp06mfpLCSESYOIrqTZJ2lCI5GXy4mP27NmMHDmSL7/8knv37uHj40PXrl35+uuvTf2lhBAvcHR0RKfTcf/+fdKnT2+SMRoGg4GoqCgiIiJs5rKL0Wjk/v37cZddhBCmZ/Liw9PTE39/f/z9/U390kKIN7C3tydz5szcuHHDZGtdGI1Gnj9/jqurq00NONXpdGTOnNkk02yFEK+ynb1dhLABHh4e5M6d22Tz9/V6PYGBgVSpUsWmegEcHR2l8BAiGUnxIYSVsbe3N9kvTnt7e6Kjo3FxcbGp4kMIkbxs4yKuEEIIIcyGFB9CCCGESFFSfAghhBAiRZndmI/Y+fWxG8zZGr1eT3h4OCEhIXKN/T1IO5qGtKNpSDuahrSjaSRXO8b+3k7MOjlmV3w8e/YMAF9fX42TCCGEECKpnj17RqpUqd74HJNvLPe+DAYDt27dsrmNrGLFbqx3/fp1m9xYz1SkHU1D2tE0pB1NQ9rRNJKrHWM3ovTx8XnrooRm1/NhZ2dH5syZtY6hOS8vL/nPZQLSjqYh7Wga0o6mIe1oGsnRjm/r8YglA06FEEIIkaKk+BBCCCFEipLiw8w4OzszatQonJ2dtY5i0aQdTUPa0TSkHU1D2tE0zKEdzW7AqRBCCCGsm/R8CCGEECJFSfEhhBBCiBQlxYcQQgghUpQUH0IIIYRIUVJ8mImJEydSunRpPD09yZAhA40bN+bs2bNax7JokyZNQqfT0bdvX62jWKSbN2/Stm1b0qZNi6urK4ULF+bQoUNax7IoMTExjBw5kuzZs+Pq6krOnDkZO3Zsova+sGWBgYE0aNAAHx8fdDod69ate+lxo9HI119/TaZMmXB1daVWrVqcP39em7Bm7E3tqNfrGTx4MIULF8bd3R0fHx/at2/PrVu3UiSbFB9mYseOHfTo0YN9+/axdetW9Ho9derUISwsTOtoFungwYN8//33FClSROsoFunx48dUrFgRR0dH/vjjD06dOsW0adP44IMPtI5mUSZPnszcuXP59ttvOX36NJMnT2bKlCnMnj1b62hmLSwsjKJFizJnzpwEH58yZQqzZs1i3rx57N+/H3d3d+rWrUtEREQKJzVvb2rH8PBwjhw5wsiRIzly5Ai//vorZ8+epWHDhikTzijM0r1794yAcceOHVpHsTjPnj0z5s6d27h161Zj1apVjX369NE6ksUZPHiwsVKlSlrHsHgff/yxsVOnTi/d17RpU2ObNm00SmR5AGNAQEDcbYPBYMyYMaPxm2++ibvvyZMnRmdnZ+OqVas0SGgZ/tuOCTlw4IARMF69ejXZ80jPh5l6+vQpAGnSpNE4ieXp0aMHH3/8MbVq1dI6isVav349pUqVokWLFmTIkIHixYszf/58rWNZnAoVKvD3339z7tw5AIKDg9m1axf16tXTOJnlunz5Mnfu3Hnp/3eqVKkoW7Yse/fu1TCZ5Xv69Ck6nY7UqVMn+9cyu43lhNrZt2/fvlSsWJFChQppHceirF69miNHjnDw4EGto1i0S5cuMXfuXPr378+wYcM4ePAgvXv3xsnJiQ4dOmgdz2IMGTKEkJAQ8uXLh729PTExMYwfP542bdpoHc1i3blzBwBvb++X7vf29o57TCRdREQEgwcP5tNPP02RTfuk+DBDPXr04MSJE+zatUvrKBbl+vXr9OnTh61bt+Li4qJ1HItmMBgoVaoUEyZMAKB48eKcOHGCefPmSfGRBD///DMrVqxg5cqVFCxYkKCgIPr27YuPj4+0ozAber2eTz75BKPRyNy5c1Pka8plFzPTs2dPNmzYwLZt28icObPWcSzK4cOHuXfvHiVKlMDBwQEHBwd27NjBrFmzcHBwICYmRuuIFiNTpkwUKFDgpfvy58/PtWvXNEpkmQYOHMiQIUNo1aoVhQsXpl27dvTr14+JEydqHc1iZcyYEYC7d+++dP/du3fjHhOJF1t4XL16la1bt6ZIrwdI8WE2jEYjPXv2JCAggH/++Yfs2bNrHcni1KxZk+PHjxMUFBR3lCpVijZt2hAUFIS9vb3WES1GxYoVX5nqfe7cObJmzapRIssUHh6Ond3LP2bt7e0xGAwaJbJ82bNnJ2PGjPz9999x94WEhLB//37Kly+vYTLLE1t4nD9/nr/++ou0adOm2NeWyy5mokePHqxcuZLffvsNT0/PuGuXqVKlwtXVVeN0lsHT0/OVMTLu7u6kTZtWxs4kUb9+/ahQoQITJkzgk08+4cCBA/zwww/88MMPWkezKA0aNGD8+PFkyZKFggULcvToUaZPn06nTp20jmbWQkNDuXDhQtzty5cvExQURJo0aciSJQt9+/Zl3Lhx5M6dm+zZszNy5Eh8fHxo3LixdqHN0JvaMVOmTDRv3pwjR46wYcMGYmJi4n7vpEmTBicnp+QNl+zzaUSiAAkeixcv1jqaRZOptu/u999/NxYqVMjo7OxszJcvn/GHH37QOpLFCQkJMfbp08eYJUsWo4uLizFHjhzG4cOHGyMjI7WOZta2bduW4M/DDh06GI1GNd125MiRRm9vb6Ozs7OxZs2axrNnz2ob2gy9qR0vX7782t8727ZtS/ZsOqNRltoTQgghRMqRMR9CCCGESFFSfAghhBAiRUnxIYQQQogUJcWHEEIIIVKUFB9CCCGESFFSfAghhBAiRUnxIYQQQogUJcWHEEIIIVKUFB9CCCGESFFSfAghhBAiRUnxIYQQQogUJcWHEEIIIVLU/wET2rg5IJUAtwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ------------------------ Finally, let's explore the climatology ------------------------                                                        \n",
    "ds   = read_dcenti_google_cloud(-4)                                      # Read data\n",
    "ts   = ds.temperature_clim.values                               # Subset temperature\n",
    "lsat = ds.lsat_clim.values                                      # Subset temperature\n",
    "sst  = ds.sst_clim.values                                       # Subset temperature\n",
    "\n",
    "GL = np.zeros((ts.shape[0],3))\n",
    "for ct in np.arange(ts.shape[0]):\n",
    "    GL[ct,0] = calculate_global_mean_temperature(ts[ct,:,:])      # Calculate global mean while weighting grids by cosine of latitude\n",
    "    GL[ct,1] = calculate_global_mean_temperature(lsat[ct,:,:])    # Calculate global mean while weighting grids by cosine of latitude\n",
    "    GL[ct,2] = calculate_global_mean_temperature(sst[ct,:,:])     # Calculate global mean while weighting grids by cosine of latitude\n",
    "\n",
    "plt.plot(np.arange(1,13),GL[:,0],color='k', label = 'TS');  \n",
    "plt.plot(np.arange(1,13),GL[:,1],color='r', label = 'LSAT');  \n",
    "plt.plot(np.arange(1,13),GL[:,2],color='b', label = 'SST');  \n",
    "plt.grid('on')\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "5d909e79-bbfc-40ba-95af-742dc5746901",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(12, 36, 72)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5b2f75a6-04f5-485f-8e51-c353ab781a4e",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python (nc_to_zarr)",
   "language": "python",
   "name": "netcdf_to_zarr"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.18"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
